{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determine period size for benchmark datasets\n",
    "\n",
    "This notebook iterates over all datasets in our benchmark dataset collection and determines the dominant period size using [`forecast.findfrequency`](https://rdrr.io/cran/forecast/man/findfrequency.html) from R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import json\n",
    "import rpy2\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare notebooks for R usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"R_HOME\"] = \"/home/sebastian.schmidl/.conda/envs/python-r/lib/R\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load R libraries (install `forecast` if not present)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian.schmidl/.conda/envs/python-r/lib/python3.7/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: During startup - \n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/sebastian.schmidl/.conda/envs/python-r/lib/python3.7/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: Warning messages:\n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/sebastian.schmidl/.conda/envs/python-r/lib/python3.7/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: 1: Setting LC_TIME failed, using \"C\" \n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/sebastian.schmidl/.conda/envs/python-r/lib/python3.7/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: 2: Setting LC_MONETARY failed, using \"C\" \n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/sebastian.schmidl/.conda/envs/python-r/lib/python3.7/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: 3: Setting LC_PAPER failed, using \"C\" \n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n",
      "/home/sebastian.schmidl/.conda/envs/python-r/lib/python3.7/site-packages/rpy2/rinterface/__init__.py:146: RRuntimeWarning: 4: Setting LC_MEASUREMENT failed, using \"C\" \n",
      "\n",
      "  warnings.warn(x, RRuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "import rpy2.robjects as robjects\n",
    "from rpy2.robjects.packages import importr\n",
    "try:\n",
    "    from rpy2.rinterface import RRuntimeError\n",
    "except ImportError:\n",
    "    from rpy2.rinterface.embedded import RRuntimeError\n",
    "\n",
    "try:\n",
    "    forecast = importr(\"forecast\")\n",
    "except ImportError:\n",
    "    utils = importr(\"utils\")\n",
    "    utils.chooseCRANmirror(ind=1)\n",
    "    utils.install_packages(robjects.StrVector([\"forecast\"]))\n",
    "    forecast = importr(\"forecast\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterate over datasets and determine their period\n",
    "\n",
    "Load dataset overview file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>...</th>\n",
       "      <th>contamination</th>\n",
       "      <th>num_anomalies</th>\n",
       "      <th>min_anomaly_length</th>\n",
       "      <th>median_anomaly_length</th>\n",
       "      <th>max_anomaly_length</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>trend</th>\n",
       "      <th>stationarity</th>\n",
       "      <th>period_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CalIt2</td>\n",
       "      <td>CalIt2-traffic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/CalIt2/CalIt2-traffic.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040873</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>3.812897</td>\n",
       "      <td>6.422468</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Daphnet</td>\n",
       "      <td>S01R01E0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/Daphnet/S01R01E0.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365.860382</td>\n",
       "      <td>263.588317</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Daphnet</td>\n",
       "      <td>S01R01E1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/Daphnet/S01R01E1.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069932</td>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>127</td>\n",
       "      <td>973</td>\n",
       "      <td>388.778434</td>\n",
       "      <td>318.691519</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daphnet</td>\n",
       "      <td>S01R02E0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/Daphnet/S01R02E0.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.053715</td>\n",
       "      <td>5</td>\n",
       "      <td>112</td>\n",
       "      <td>256</td>\n",
       "      <td>571</td>\n",
       "      <td>368.768492</td>\n",
       "      <td>318.540618</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Daphnet</td>\n",
       "      <td>S02R01E0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/Daphnet/S02R01E0.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138164</td>\n",
       "      <td>9</td>\n",
       "      <td>60</td>\n",
       "      <td>440</td>\n",
       "      <td>911</td>\n",
       "      <td>357.262886</td>\n",
       "      <td>401.863439</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>WebscopeS5</td>\n",
       "      <td>A4Benchmark-95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/WebscopeS5/A4Benchmark-95.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1682.450826</td>\n",
       "      <td>1000.344063</td>\n",
       "      <td>linear trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>WebscopeS5</td>\n",
       "      <td>A4Benchmark-96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/WebscopeS5/A4Benchmark-96.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1992.507175</td>\n",
       "      <td>1073.141772</td>\n",
       "      <td>linear trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>WebscopeS5</td>\n",
       "      <td>A4Benchmark-97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/WebscopeS5/A4Benchmark-97.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010119</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-633.574096</td>\n",
       "      <td>863.324620</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>WebscopeS5</td>\n",
       "      <td>A4Benchmark-98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/WebscopeS5/A4Benchmark-98.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010119</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2468.960650</td>\n",
       "      <td>1400.068899</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>WebscopeS5</td>\n",
       "      <td>A4Benchmark-99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/WebscopeS5/A4Benchmark-99.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011310</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>276.595691</td>\n",
       "      <td>411.585768</td>\n",
       "      <td>no trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1169 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     collection_name    dataset_name train_path  \\\n",
       "0             CalIt2  CalIt2-traffic        NaN   \n",
       "1            Daphnet        S01R01E0        NaN   \n",
       "2            Daphnet        S01R01E1        NaN   \n",
       "3            Daphnet        S01R02E0        NaN   \n",
       "4            Daphnet        S02R01E0        NaN   \n",
       "...              ...             ...        ...   \n",
       "1164      WebscopeS5  A4Benchmark-95        NaN   \n",
       "1165      WebscopeS5  A4Benchmark-96        NaN   \n",
       "1166      WebscopeS5  A4Benchmark-97        NaN   \n",
       "1167      WebscopeS5  A4Benchmark-98        NaN   \n",
       "1168      WebscopeS5  A4Benchmark-99        NaN   \n",
       "\n",
       "                                          test_path dataset_type  \\\n",
       "0       multivariate/CalIt2/CalIt2-traffic.test.csv         real   \n",
       "1            multivariate/Daphnet/S01R01E0.test.csv         real   \n",
       "2            multivariate/Daphnet/S01R01E1.test.csv         real   \n",
       "3            multivariate/Daphnet/S01R02E0.test.csv         real   \n",
       "4            multivariate/Daphnet/S02R01E0.test.csv         real   \n",
       "...                                             ...          ...   \n",
       "1164  univariate/WebscopeS5/A4Benchmark-95.test.csv    synthetic   \n",
       "1165  univariate/WebscopeS5/A4Benchmark-96.test.csv    synthetic   \n",
       "1166  univariate/WebscopeS5/A4Benchmark-97.test.csv    synthetic   \n",
       "1167  univariate/WebscopeS5/A4Benchmark-98.test.csv    synthetic   \n",
       "1168  univariate/WebscopeS5/A4Benchmark-99.test.csv    synthetic   \n",
       "\n",
       "      datetime_index  split_at    train_type  train_is_normal    input_type  \\\n",
       "0               True       NaN  unsupervised            False  multivariate   \n",
       "1               True       NaN  unsupervised            False  multivariate   \n",
       "2               True       NaN  unsupervised            False  multivariate   \n",
       "3               True       NaN  unsupervised            False  multivariate   \n",
       "4               True       NaN  unsupervised            False  multivariate   \n",
       "...              ...       ...           ...              ...           ...   \n",
       "1164            True       NaN  unsupervised            False    univariate   \n",
       "1165            True       NaN  unsupervised            False    univariate   \n",
       "1166            True       NaN  unsupervised            False    univariate   \n",
       "1167            True       NaN  unsupervised            False    univariate   \n",
       "1168            True       NaN  unsupervised            False    univariate   \n",
       "\n",
       "      ...  contamination  num_anomalies  min_anomaly_length  \\\n",
       "0     ...       0.040873             29                   2   \n",
       "1     ...       0.000000              0                   0   \n",
       "2     ...       0.069932             18                  34   \n",
       "3     ...       0.053715              5                 112   \n",
       "4     ...       0.138164              9                  60   \n",
       "...   ...            ...            ...                 ...   \n",
       "1164  ...       0.004762              8                   1   \n",
       "1165  ...       0.002976              5                   1   \n",
       "1166  ...       0.010119             17                   1   \n",
       "1167  ...       0.010119             17                   1   \n",
       "1168  ...       0.011310             18                   1   \n",
       "\n",
       "      median_anomaly_length  max_anomaly_length         mean       stddev  \\\n",
       "0                         7                  19     3.812897     6.422468   \n",
       "1                         0                   0   365.860382   263.588317   \n",
       "2                       127                 973   388.778434   318.691519   \n",
       "3                       256                 571   368.768492   318.540618   \n",
       "4                       440                 911   357.262886   401.863439   \n",
       "...                     ...                 ...          ...          ...   \n",
       "1164                      1                   1  1682.450826  1000.344063   \n",
       "1165                      1                   1  1992.507175  1073.141772   \n",
       "1166                      1                   1  -633.574096   863.324620   \n",
       "1167                      1                   1  2468.960650  1400.068899   \n",
       "1168                      1                   2   276.595691   411.585768   \n",
       "\n",
       "             trend           stationarity period_size  \n",
       "0         no trend  difference_stationary        48.0  \n",
       "1         no trend  difference_stationary        36.0  \n",
       "2         no trend             stationary        32.0  \n",
       "3         no trend             stationary        34.0  \n",
       "4         no trend             stationary        29.0  \n",
       "...            ...                    ...         ...  \n",
       "1164  linear trend       trend_stationary        12.0  \n",
       "1165  linear trend       trend_stationary        12.0  \n",
       "1166   kubic trend       trend_stationary        12.0  \n",
       "1167   kubic trend       trend_stationary        12.0  \n",
       "1168      no trend       trend_stationary        12.0  \n",
       "\n",
       "[1169 rows x 22 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_path = Path(\"/home/projects/akita/data/benchmark-data/data-processed\")\n",
    "df_datasets = pd.read_csv(root_path / \"datasets.csv\")\n",
    "df_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform period size extraction and store it in the metadata files and the dataset overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_frequency(values):\n",
    "    try:\n",
    "        x = robjects.FloatVector(values.tolist())\n",
    "        return forecast.findfrequency(x)[0]\n",
    "    except RRuntimeError as ex:\n",
    "        if \"zero-variance series\" in str(ex) or \"'order.max' must be >= 1\" in str(ex) or \"Error in lm.fit\" in str(ex):\n",
    "            return 1\n",
    "        else:\n",
    "            raise ex\n",
    "\n",
    "def get_metadata_filename(dataset_path):\n",
    "    filename_base = \".\".join(dataset_path.stem.split(\".\")[:-1])\n",
    "    return dataset_path.parent / f\"{filename_base}.metadata.json\"\n",
    "\n",
    "def find_periods(dataset_path):\n",
    "    df = pd.read_csv(dataset_path, parse_dates=True, infer_datetime_format=True)\n",
    "    periods = []\n",
    "    features = set(df.columns) - {\"timestamp\", \"is_anomaly\"}\n",
    "    for f in features:\n",
    "        period = find_frequency(df[f])\n",
    "        periods.append(period)\n",
    "    periods = [int(p) for p in periods]\n",
    "    return list(zip(features, periods))\n",
    "\n",
    "def aggregate_periods(periods):\n",
    "    periods = np.array([p for (f, p) in periods])\n",
    "    hist = np.bincount(periods)\n",
    "    mode = hist.argmax()\n",
    "    if hist[mode] < 2:\n",
    "        mode = np.median(periods)\n",
    "    return int(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Saving dataset overview!\n",
      "Processing NormA/Discords_annsgun ...\n",
      "  ... test dataset: [111]\n",
      "  ... period = 111\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/Discords_dutch_power_demand ...\n",
      "  ... test dataset: [111]\n",
      "  ... period = 111\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/Discords_marotta_valve_tek_14 ...\n",
      "  ... test dataset: [1]\n",
      "  ... period = 1\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/Discords_marotta_valve_tek_16 ...\n",
      "  ... test dataset: [1]\n",
      "  ... period = 1\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/Discords_marotta_valve_tek_17 ...\n",
      "  ... test dataset: [1]\n",
      "  ... period = 1\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/Discords_patient_respiration1 ...\n",
      "  ... test dataset: [22]\n",
      "  ... period = 22\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/Discords_patient_respiration2 ...\n",
      "  ... test dataset: [40]\n",
      "  ... period = 40\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_104000_AnomalyL_200_AnomalyN_20_NoisePerc_0 ...\n",
      "  ... test dataset: [250]\n",
      "  ... period = 250\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_106000_AnomalyL_100_AnomalyN_60_NoisePerc_0 ...\n",
      "  ... test dataset: [250]\n",
      "  ... period = 250\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_108000_AnomalyL_200_AnomalyN_40_NoisePerc_0 ...\n",
      "  ... test dataset: [250]\n",
      "  ... period = 250\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_0 ...\n",
      "  ... test dataset: [250]\n",
      "  ... period = 250\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_10 ...\n",
      "  ... test dataset: [333]\n",
      "  ... period = 333\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_15 ...\n",
      "  ... test dataset: [250]\n",
      "  ... period = 250\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_20 ...\n",
      "  ... test dataset: [200]\n",
      "  ... period = 200\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_25 ...\n",
      "  ... test dataset: [333]\n",
      "  ... period = 333\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_112000_AnomalyL_200_AnomalyN_60_NoisePerc_5 ...\n",
      "  ... test dataset: [333]\n",
      "  ... period = 333\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_116000_AnomalyL_200_AnomalyN_80_NoisePerc_0 ...\n",
      "  ... test dataset: [250]\n",
      "  ... period = 250\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_120000_AnomalyL_200_AnomalyN_100_NoisePerc_0 ...\n",
      "  ... test dataset: [333]\n",
      "  ... period = 333\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_124000_AnomalyL_400_AnomalyN_60_NoisePerc_0 ...\n",
      "  ... test dataset: [166]\n",
      "  ... period = 166\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_148000_AnomalyL_800_AnomalyN_60_NoisePerc_0 ...\n",
      "  ... test dataset: [34]\n",
      "  ... period = 34\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "Processing NormA/SinusRW_Length_196000_AnomalyL_1600_AnomalyN_60_NoisePerc_0 ...\n",
      "  ... test dataset: [36]\n",
      "  ... period = 36\n",
      "  ... writing metadata file\n",
      "finished.\n",
      "\n",
      "Saving dataset overview!\n"
     ]
    }
   ],
   "source": [
    "def process_dataset(dataset_path, metas, is_train = False):\n",
    "    periods = find_periods(dataset_path)\n",
    "    print(f\"  ... {'train' if is_train else 'test'} dataset: {[p for (f, p) in periods]}\")\n",
    "    for meta in metas:\n",
    "        if meta[\"is_train\"] == is_train:\n",
    "            meta[\"periods\"] = dict(periods)\n",
    "    if is_train == False:\n",
    "        return aggregate_periods(periods)\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "current_collection = None\n",
    "selected_collection = \"NormA\"\n",
    "for i, row in df_datasets.iterrows():\n",
    "    if selected_collection is not None and row[\"collection_name\"] != selected_collection:\n",
    "        continue\n",
    "    if not np.isnan(row[\"period_size\"]):\n",
    "        print(f\"Skipping {row['collection_name']}/{row['dataset_name']}, because it already has a period ({row['period_size']})\")\n",
    "        continue\n",
    "    if row[\"collection_name\"] == \"Kitsune\":\n",
    "        print(f\"Skipping {row['collection_name']}/{row['dataset_name']}, because it would take too long!\")\n",
    "        continue\n",
    "    \n",
    "    if current_collection != row[\"collection_name\"]:\n",
    "        # store dataset overview\n",
    "        print(\"### Saving dataset overview!\")\n",
    "        df_datasets.to_csv(root_path / \"datasets.csv\", index=False)\n",
    "    current_collection = row[\"collection_name\"]\n",
    "\n",
    "    print(f\"Processing {row['collection_name']}/{row['dataset_name']} ...\")\n",
    "    dataset_path = root_path / row[\"test_path\"]\n",
    "    # load metadata\n",
    "    meta_filename = get_metadata_filename(dataset_path)\n",
    "    try:\n",
    "        with meta_filename.open(\"r\") as fh:\n",
    "            metas = json.load(fh)\n",
    "        skip_meta = False\n",
    "    except FileNotFoundError:\n",
    "        print(\"  ... skipping metadata files!\")\n",
    "        skip_meta = True\n",
    "        metas = []\n",
    "        \n",
    "    # for test dataset\n",
    "    period = process_dataset(dataset_path, metas, is_train=False)\n",
    "    \n",
    "    # for train dataset (if exist)\n",
    "    if row[\"train_type\"] != \"unsupervised\" and not skip_meta:\n",
    "        dataset_path = root_path / row[\"train_path\"]\n",
    "        process_dataset(dataset_path, metas, is_train=True)\n",
    "    \n",
    "    print(f\"  ... period = {period}\")\n",
    "    \n",
    "    # store metadata\n",
    "    if not skip_meta:\n",
    "        print(\"  ... writing metadata file\")\n",
    "        with meta_filename.open(\"w\") as fh:\n",
    "            json.dump(metas, fh, indent=2, sort_keys=True)\n",
    "\n",
    "    # adapt dataset overview\n",
    "    df_datasets.loc[i, \"period_size\"] = period\n",
    "    print(\"finished.\")\n",
    "\n",
    "# store dataset overview\n",
    "print(\"\\nSaving dataset overview!\")\n",
    "df_datasets.to_csv(root_path / \"datasets.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace periods of size `1` with `NaN` (because then our fallback values come into play)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_path = Path(\"/home/projects/akita/data/benchmark-data/data-processed\")\n",
    "df_datasets = pd.read_csv(root_path / \"datasets.csv\")\n",
    "\n",
    "indexer = df_datasets[\"period_size\"] == 1\n",
    "df_datasets.loc[indexer, \"period_size\"] = np.nan\n",
    "\n",
    "df_datasets.to_csv(root_path / \"datasets.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>...</th>\n",
       "      <th>contamination</th>\n",
       "      <th>num_anomalies</th>\n",
       "      <th>min_anomaly_length</th>\n",
       "      <th>median_anomaly_length</th>\n",
       "      <th>max_anomaly_length</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>trend</th>\n",
       "      <th>stationarity</th>\n",
       "      <th>period_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>NormA</td>\n",
       "      <td>Discords_annsgun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_annsgun.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>3.097742e+02</td>\n",
       "      <td>96.244237</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>NormA</td>\n",
       "      <td>Discords_dutch_power_demand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_dutch_power_demand.t...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.091324</td>\n",
       "      <td>4</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>1.144038e+03</td>\n",
       "      <td>292.725791</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>NormA</td>\n",
       "      <td>Discords_marotta_valve_tek_14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_marotta_valve_tek_14...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051200</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>1.120064e+00</td>\n",
       "      <td>1.703123</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>NormA</td>\n",
       "      <td>Discords_marotta_valve_tek_16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_marotta_valve_tek_16...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025600</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>1.054768e+00</td>\n",
       "      <td>1.665258</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>NormA</td>\n",
       "      <td>Discords_marotta_valve_tek_17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_marotta_valve_tek_17...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025600</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>9.959120e-01</td>\n",
       "      <td>1.626007</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>NormA</td>\n",
       "      <td>Discords_patient_respiration1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_patient_respiration1...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030769</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>7.452095e+01</td>\n",
       "      <td>289.473775</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>NormA</td>\n",
       "      <td>Discords_patient_respiration2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/Discords_patient_respiration2...</td>\n",
       "      <td>real</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>150</td>\n",
       "      <td>7.977460e+01</td>\n",
       "      <td>77.999132</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_104000_AnomalyL_200_AnomalyN_20...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_104000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>20</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-6.943642e-15</td>\n",
       "      <td>1.413004</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_106000_AnomalyL_100_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_106000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056604</td>\n",
       "      <td>60</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>-5.439808e-15</td>\n",
       "      <td>1.414737</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_108000_AnomalyL_200_AnomalyN_40...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_108000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>40</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-1.370558e-15</td>\n",
       "      <td>1.412455</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>9.062465e-15</td>\n",
       "      <td>1.413815</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>5.083426e-15</td>\n",
       "      <td>1.411193</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-1.226194e-14</td>\n",
       "      <td>1.412934</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-4.125208e-15</td>\n",
       "      <td>1.413281</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-8.619898e-15</td>\n",
       "      <td>1.410894</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_112000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107143</td>\n",
       "      <td>60</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-8.522453e-15</td>\n",
       "      <td>1.411797</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_116000_AnomalyL_200_AnomalyN_80...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_116000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>80</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-7.126989e-15</td>\n",
       "      <td>1.413798</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_120000_AnomalyL_200_AnomalyN_10...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_120000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>-4.704740e-15</td>\n",
       "      <td>1.413511</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_124000_AnomalyL_400_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_124000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193548</td>\n",
       "      <td>60</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>3.163061e-15</td>\n",
       "      <td>1.414285</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_148000_AnomalyL_800_AnomalyN_60...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_148000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>60</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>-7.988805e-17</td>\n",
       "      <td>1.414400</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>NormA</td>\n",
       "      <td>SinusRW_Length_196000_AnomalyL_1600_AnomalyN_6...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/NormA/SinusRW_Length_196000_Anomaly...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>...</td>\n",
       "      <td>0.489796</td>\n",
       "      <td>60</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>1600</td>\n",
       "      <td>-2.561434e-15</td>\n",
       "      <td>1.411909</td>\n",
       "      <td>no trend</td>\n",
       "      <td>stationary</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    collection_name                                       dataset_name  \\\n",
       "621           NormA                                   Discords_annsgun   \n",
       "622           NormA                        Discords_dutch_power_demand   \n",
       "623           NormA                      Discords_marotta_valve_tek_14   \n",
       "624           NormA                      Discords_marotta_valve_tek_16   \n",
       "625           NormA                      Discords_marotta_valve_tek_17   \n",
       "626           NormA                      Discords_patient_respiration1   \n",
       "627           NormA                      Discords_patient_respiration2   \n",
       "628           NormA  SinusRW_Length_104000_AnomalyL_200_AnomalyN_20...   \n",
       "629           NormA  SinusRW_Length_106000_AnomalyL_100_AnomalyN_60...   \n",
       "630           NormA  SinusRW_Length_108000_AnomalyL_200_AnomalyN_40...   \n",
       "631           NormA  SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...   \n",
       "632           NormA  SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...   \n",
       "633           NormA  SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...   \n",
       "634           NormA  SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...   \n",
       "635           NormA  SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...   \n",
       "636           NormA  SinusRW_Length_112000_AnomalyL_200_AnomalyN_60...   \n",
       "637           NormA  SinusRW_Length_116000_AnomalyL_200_AnomalyN_80...   \n",
       "638           NormA  SinusRW_Length_120000_AnomalyL_200_AnomalyN_10...   \n",
       "639           NormA  SinusRW_Length_124000_AnomalyL_400_AnomalyN_60...   \n",
       "640           NormA  SinusRW_Length_148000_AnomalyL_800_AnomalyN_60...   \n",
       "641           NormA  SinusRW_Length_196000_AnomalyL_1600_AnomalyN_6...   \n",
       "\n",
       "    train_path                                          test_path  \\\n",
       "621        NaN         univariate/NormA/Discords_annsgun.test.csv   \n",
       "622        NaN  univariate/NormA/Discords_dutch_power_demand.t...   \n",
       "623        NaN  univariate/NormA/Discords_marotta_valve_tek_14...   \n",
       "624        NaN  univariate/NormA/Discords_marotta_valve_tek_16...   \n",
       "625        NaN  univariate/NormA/Discords_marotta_valve_tek_17...   \n",
       "626        NaN  univariate/NormA/Discords_patient_respiration1...   \n",
       "627        NaN  univariate/NormA/Discords_patient_respiration2...   \n",
       "628        NaN  univariate/NormA/SinusRW_Length_104000_Anomaly...   \n",
       "629        NaN  univariate/NormA/SinusRW_Length_106000_Anomaly...   \n",
       "630        NaN  univariate/NormA/SinusRW_Length_108000_Anomaly...   \n",
       "631        NaN  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "632        NaN  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "633        NaN  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "634        NaN  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "635        NaN  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "636        NaN  univariate/NormA/SinusRW_Length_112000_Anomaly...   \n",
       "637        NaN  univariate/NormA/SinusRW_Length_116000_Anomaly...   \n",
       "638        NaN  univariate/NormA/SinusRW_Length_120000_Anomaly...   \n",
       "639        NaN  univariate/NormA/SinusRW_Length_124000_Anomaly...   \n",
       "640        NaN  univariate/NormA/SinusRW_Length_148000_Anomaly...   \n",
       "641        NaN  univariate/NormA/SinusRW_Length_196000_Anomaly...   \n",
       "\n",
       "    dataset_type  datetime_index  split_at    train_type  train_is_normal  \\\n",
       "621         real           False       NaN  unsupervised            False   \n",
       "622         real           False       NaN  unsupervised            False   \n",
       "623         real           False       NaN  unsupervised            False   \n",
       "624         real           False       NaN  unsupervised            False   \n",
       "625         real           False       NaN  unsupervised            False   \n",
       "626         real           False       NaN  unsupervised            False   \n",
       "627         real           False       NaN  unsupervised            False   \n",
       "628    synthetic           False       NaN  unsupervised            False   \n",
       "629    synthetic           False       NaN  unsupervised            False   \n",
       "630    synthetic           False       NaN  unsupervised            False   \n",
       "631    synthetic           False       NaN  unsupervised            False   \n",
       "632    synthetic           False       NaN  unsupervised            False   \n",
       "633    synthetic           False       NaN  unsupervised            False   \n",
       "634    synthetic           False       NaN  unsupervised            False   \n",
       "635    synthetic           False       NaN  unsupervised            False   \n",
       "636    synthetic           False       NaN  unsupervised            False   \n",
       "637    synthetic           False       NaN  unsupervised            False   \n",
       "638    synthetic           False       NaN  unsupervised            False   \n",
       "639    synthetic           False       NaN  unsupervised            False   \n",
       "640    synthetic           False       NaN  unsupervised            False   \n",
       "641    synthetic           False       NaN  unsupervised            False   \n",
       "\n",
       "     input_type  ...  contamination  num_anomalies  min_anomaly_length  \\\n",
       "621  univariate  ...       0.075000              1                 150   \n",
       "622  univariate  ...       0.091324              4                 800   \n",
       "623  univariate  ...       0.051200              2                 128   \n",
       "624  univariate  ...       0.025600              1                 128   \n",
       "625  univariate  ...       0.025600              1                 128   \n",
       "626  univariate  ...       0.030769              2                 100   \n",
       "627  univariate  ...       0.075000              2                 150   \n",
       "628  univariate  ...       0.038462             20                 200   \n",
       "629  univariate  ...       0.056604             60                 100   \n",
       "630  univariate  ...       0.074074             40                 200   \n",
       "631  univariate  ...       0.107143             60                 200   \n",
       "632  univariate  ...       0.107143             60                 200   \n",
       "633  univariate  ...       0.107143             60                 200   \n",
       "634  univariate  ...       0.107143             60                 200   \n",
       "635  univariate  ...       0.107143             60                 200   \n",
       "636  univariate  ...       0.107143             60                 200   \n",
       "637  univariate  ...       0.137931             80                 200   \n",
       "638  univariate  ...       0.166667            100                 200   \n",
       "639  univariate  ...       0.193548             60                 400   \n",
       "640  univariate  ...       0.324324             60                 800   \n",
       "641  univariate  ...       0.489796             60                1600   \n",
       "\n",
       "     median_anomaly_length  max_anomaly_length          mean      stddev  \\\n",
       "621                    150                 150  3.097742e+02   96.244237   \n",
       "622                    800                 800  1.144038e+03  292.725791   \n",
       "623                    128                 128  1.120064e+00    1.703123   \n",
       "624                    128                 128  1.054768e+00    1.665258   \n",
       "625                    128                 128  9.959120e-01    1.626007   \n",
       "626                    100                 100  7.452095e+01  289.473775   \n",
       "627                    150                 150  7.977460e+01   77.999132   \n",
       "628                    200                 200 -6.943642e-15    1.413004   \n",
       "629                    100                 100 -5.439808e-15    1.414737   \n",
       "630                    200                 200 -1.370558e-15    1.412455   \n",
       "631                    200                 200  9.062465e-15    1.413815   \n",
       "632                    200                 200  5.083426e-15    1.411193   \n",
       "633                    200                 200 -1.226194e-14    1.412934   \n",
       "634                    200                 200 -4.125208e-15    1.413281   \n",
       "635                    200                 200 -8.619898e-15    1.410894   \n",
       "636                    200                 200 -8.522453e-15    1.411797   \n",
       "637                    200                 200 -7.126989e-15    1.413798   \n",
       "638                    200                 200 -4.704740e-15    1.413511   \n",
       "639                    400                 400  3.163061e-15    1.414285   \n",
       "640                    800                 800 -7.988805e-17    1.414400   \n",
       "641                   1600                1600 -2.561434e-15    1.411909   \n",
       "\n",
       "        trend           stationarity period_size  \n",
       "621  no trend  difference_stationary       111.0  \n",
       "622  no trend             stationary       111.0  \n",
       "623  no trend  difference_stationary         NaN  \n",
       "624  no trend  difference_stationary         NaN  \n",
       "625  no trend  difference_stationary         NaN  \n",
       "626  no trend             stationary        22.0  \n",
       "627  no trend             stationary        40.0  \n",
       "628  no trend             stationary       250.0  \n",
       "629  no trend             stationary       250.0  \n",
       "630  no trend             stationary       250.0  \n",
       "631  no trend             stationary       250.0  \n",
       "632  no trend             stationary       333.0  \n",
       "633  no trend             stationary       250.0  \n",
       "634  no trend             stationary       200.0  \n",
       "635  no trend             stationary       333.0  \n",
       "636  no trend             stationary       333.0  \n",
       "637  no trend             stationary       250.0  \n",
       "638  no trend             stationary       333.0  \n",
       "639  no trend             stationary       166.0  \n",
       "640  no trend             stationary        34.0  \n",
       "641  no trend             stationary        36.0  \n",
       "\n",
       "[21 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_datasets[df_datasets[\"collection_name\"] == \"NormA\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>ankle_horiz_fwd</th>\n",
       "      <th>ankle_vert</th>\n",
       "      <th>ankle_horiz_lateral</th>\n",
       "      <th>leg_horiz_fwd</th>\n",
       "      <th>leg_vert</th>\n",
       "      <th>leg_horiz_lateral</th>\n",
       "      <th>trunk_horiz_fwd</th>\n",
       "      <th>trunk_vert</th>\n",
       "      <th>trunk_horiz_lateral</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-01-01 00:12:30.000</td>\n",
       "      <td>-30</td>\n",
       "      <td>990</td>\n",
       "      <td>326</td>\n",
       "      <td>-45</td>\n",
       "      <td>972</td>\n",
       "      <td>181</td>\n",
       "      <td>-38</td>\n",
       "      <td>1000</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01 00:12:30.015</td>\n",
       "      <td>-30</td>\n",
       "      <td>1000</td>\n",
       "      <td>356</td>\n",
       "      <td>-18</td>\n",
       "      <td>981</td>\n",
       "      <td>212</td>\n",
       "      <td>-48</td>\n",
       "      <td>1028</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-01-01 00:12:30.031</td>\n",
       "      <td>-20</td>\n",
       "      <td>990</td>\n",
       "      <td>336</td>\n",
       "      <td>18</td>\n",
       "      <td>981</td>\n",
       "      <td>222</td>\n",
       "      <td>-38</td>\n",
       "      <td>1038</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-01-01 00:12:30.046</td>\n",
       "      <td>-20</td>\n",
       "      <td>1000</td>\n",
       "      <td>316</td>\n",
       "      <td>36</td>\n",
       "      <td>990</td>\n",
       "      <td>222</td>\n",
       "      <td>-19</td>\n",
       "      <td>1038</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-01-01 00:12:30.062</td>\n",
       "      <td>0</td>\n",
       "      <td>990</td>\n",
       "      <td>316</td>\n",
       "      <td>36</td>\n",
       "      <td>990</td>\n",
       "      <td>212</td>\n",
       "      <td>-29</td>\n",
       "      <td>1038</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19195</th>\n",
       "      <td>1970-01-01 00:17:29.921</td>\n",
       "      <td>-353</td>\n",
       "      <td>960</td>\n",
       "      <td>237</td>\n",
       "      <td>145</td>\n",
       "      <td>962</td>\n",
       "      <td>181</td>\n",
       "      <td>-77</td>\n",
       "      <td>1000</td>\n",
       "      <td>-19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19196</th>\n",
       "      <td>1970-01-01 00:17:29.937</td>\n",
       "      <td>-323</td>\n",
       "      <td>960</td>\n",
       "      <td>297</td>\n",
       "      <td>54</td>\n",
       "      <td>981</td>\n",
       "      <td>202</td>\n",
       "      <td>-58</td>\n",
       "      <td>980</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19197</th>\n",
       "      <td>1970-01-01 00:17:29.953</td>\n",
       "      <td>-303</td>\n",
       "      <td>960</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "      <td>1046</td>\n",
       "      <td>222</td>\n",
       "      <td>-58</td>\n",
       "      <td>961</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19198</th>\n",
       "      <td>1970-01-01 00:17:29.968</td>\n",
       "      <td>-272</td>\n",
       "      <td>980</td>\n",
       "      <td>297</td>\n",
       "      <td>-172</td>\n",
       "      <td>990</td>\n",
       "      <td>191</td>\n",
       "      <td>-58</td>\n",
       "      <td>971</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19199</th>\n",
       "      <td>1970-01-01 00:17:29.984</td>\n",
       "      <td>-252</td>\n",
       "      <td>970</td>\n",
       "      <td>287</td>\n",
       "      <td>-163</td>\n",
       "      <td>990</td>\n",
       "      <td>212</td>\n",
       "      <td>-38</td>\n",
       "      <td>980</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19200 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     timestamp  ankle_horiz_fwd  ankle_vert  \\\n",
       "0      1970-01-01 00:12:30.000              -30         990   \n",
       "1      1970-01-01 00:12:30.015              -30        1000   \n",
       "2      1970-01-01 00:12:30.031              -20         990   \n",
       "3      1970-01-01 00:12:30.046              -20        1000   \n",
       "4      1970-01-01 00:12:30.062                0         990   \n",
       "...                        ...              ...         ...   \n",
       "19195  1970-01-01 00:17:29.921             -353         960   \n",
       "19196  1970-01-01 00:17:29.937             -323         960   \n",
       "19197  1970-01-01 00:17:29.953             -303         960   \n",
       "19198  1970-01-01 00:17:29.968             -272         980   \n",
       "19199  1970-01-01 00:17:29.984             -252         970   \n",
       "\n",
       "       ankle_horiz_lateral  leg_horiz_fwd  leg_vert  leg_horiz_lateral  \\\n",
       "0                      326            -45       972                181   \n",
       "1                      356            -18       981                212   \n",
       "2                      336             18       981                222   \n",
       "3                      316             36       990                222   \n",
       "4                      316             36       990                212   \n",
       "...                    ...            ...       ...                ...   \n",
       "19195                  237            145       962                181   \n",
       "19196                  297             54       981                202   \n",
       "19197                  287              0      1046                222   \n",
       "19198                  297           -172       990                191   \n",
       "19199                  287           -163       990                212   \n",
       "\n",
       "       trunk_horiz_fwd  trunk_vert  trunk_horiz_lateral  is_anomaly  \n",
       "0                  -38        1000                   29           0  \n",
       "1                  -48        1028                   29           0  \n",
       "2                  -38        1038                    9           0  \n",
       "3                  -19        1038                    9           0  \n",
       "4                  -29        1038                   29           0  \n",
       "...                ...         ...                  ...         ...  \n",
       "19195              -77        1000                  -19           0  \n",
       "19196              -58         980                   -9           0  \n",
       "19197              -58         961                   19           0  \n",
       "19198              -58         971                   -9           0  \n",
       "19199              -38         980                   19           0  \n",
       "\n",
       "[19200 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_path = root_path / df_datasets.loc[1, \"test_path\"]\n",
    "df = pd.read_csv(dataset_path, parse_dates=True, infer_datetime_format=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJoAAAHSCAYAAACkWqEgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd5hU5fmw7zNte2WXpUuT3rsKSIwFFRCwgcYWE6wRf1Ej+YyKqIRYI9ZYwEpUDCpYsYEdpCkgIB2WtmyvMzvlfH9M2Z1edmZndnnu61L2nPOW55Q5532f9ymKqqoIgiAIgiAIgiAIgiAIQlPRxFsAQRAEQRAEQRAEQRAEoXUgiiZBEARBEARBEARBEAQhKoiiSRAEQRAEQRAEQRAEQYgKomgSBEEQBEEQBEEQBEEQooIomgRBEARBEARBEARBEISoIIomQRAEQRAEQRAEQRAEISro4i1AU8nLy1O7du0abzEEQRAEQRAEQRAEQRBaDevXry9WVTU/3HotXtHUtWtX1q1bF28xBEEQBEEQBEEQBEEQWg2KouyPpJ64zgmCIAiCIAiCIAiCIAhRQRRNgiAIgiAIgiAIgiAIQlQQRZMgCIIgCIIgCIIgCIIQFVp8jCZfmM1mCgsLMRqN8RZFiBPJycl06tQJvV4fb1EEQRAEQRAEQRAE4YShVSqaCgsLycjIoGvXriiKEm9xhGZGVVVKSkooLCykW7du8RZHEARBEARBEARBEE4YWqXrnNFopE2bNqJkOkFRFIU2bdqIRZsgCIIgCIIgCIIgNDOtUtEEiJLpBEfuvyAIgiAIgiAIgiA0P61W0SQIgiAIgiAIgiAIgiA0L6JoShCuvvpq3nnnHa/9EyZMYN26dWG317VrV4qLiyOWZ926ddxyyy1h15s5cyaDBg3i8ccfD6vevn37GDBgQNj9CYIgCIIgCIIgCIKQOLTKYOBC07BYLIwYMYIRI0aEVe/o0aN8//337N+/P0aSCYIgCIIgCIIgCIKQyLR6RdN9K7by6+HKqLbZr0Mm907uH7Tc1KlTOXjwIEajkdmzZzNr1izS09OZPXs2H3zwASkpKbz//vsUFBS41bv77rs5ePAgixYtctu/cuVK7r33XkwmEz169GDx4sWkp6f77f/JJ59kxYoVmM1mli5dSp8+fSgtLeWPf/wje/bsITU1leeff55BgwYxd+5cDh8+zL59+8jLy2PWrFk88sgjfPDBB5x33nkcPnwYgL1797Jw4UKuuuoqr/7OPvtsioqKGDJkCE888QRPPPEEy5Yt4/3332fGjBlUVFRgs9no168fe/bsYf369fzxj38kNTWVsWPHhnLpBUEQBEEQBEEQBEFIYMR1LoYsWrSI9evXs27dOhYuXEhJSQk1NTWMGTOGn3/+mfHjx/PCCy+41fnb3/5GUVERixcvRqNpuD3FxcU88MADfP7552zYsIERI0bw2GOPBew/Ly+PDRs2cMMNN/DII48AcO+99zJ06FB++eUX5s+fz5VXXukqv379et5//32WLFni1s5HH33Epk2beOmllzjppJOYOnWqz/6WL19Ojx492LRpE6eddhobN24E4JtvvmHAgAH89NNPrFmzhtGjRwNwzTXXsHDhQn744YfQLqggCIIgCIIgCIIgCAlNq7doCsXyKFYsXLiQd999F4CDBw+yc+dODAYDkyZNAmD48OF89tlnrvL3338/o0eP5vnnn/dq68cff+TXX3/ltNNOA6C+vp5TTjklYP/Tp0939bNs2TIAvv32W/73v/8BcMYZZ1BSUkJFRQUAU6ZMISUlxWdbxcXFXHHFFbz99ttkZWUFPXedTkfPnj3Ztm0ba9eu5a9//Stff/01VquVcePGUVFRQXl5OaeffjoAV1xxBR9//HHQdgVBEARBEARBEARBSFxavaIpXqxatYrPP/+cH374gdTUVCZMmIDRaESv16MoCgBarRaLxeKqM3LkSNavX09paSm5ublu7amqyllnncV///vfkGVISkry6kdVVa9yTnnS0tJ8tmO1WpkxYwb33HNPWAG7x40bx8cff4xer+fMM8/k6quvxmq18sgjj6CqqqtfQRAEQRAEQRAEQRBaB+I6FyMqKirIyckhNTWV7du38+OPPwatM3HiRObMmcP5559PVVWV27ExY8bw3XffsWvXLgBqa2v57bffwpZr/PjxvPHGG4BdGZaXl0dmZmbAOnPmzGHQoEHMmDEj7L7+/e9/c8opp5Cfn09JSQnbt2+nf//+ZGdnk5WVxbfffgvgkkkQBEEQBEEQBEEQhJaLWDTFiIkTJ/Lcc88xaNAgevfuzZgxY0Kqd/HFF1NVVcWUKVP46KOPXPvz8/N5+eWXmTlzJiaTCYAHHniAXr16hSXX3Llzueaaaxg0aBCpqam88sorQes88sgj9O/fnyFDhgAwb948pkyZErTe6NGjOXbsGOPHjwdg0KBBtG3b1mXJtHjxYlcw8HPOOSes8xAEQRAEQRAEQRAEIfFQfLlStSRGjBihrlu3zm3ftm3b6Nu3b5wkEhIFeQ4EQRAEQRAEQRAEITIURVmvquqIcOuJ65wgCIIghMCxSiOnP/wVB0tr4y2KIAiCIAiCICQs4jqXIOzYscPvsdraWlJTU30eu/nmmyksLHTbd9tttzFu3Liw2wqVb775hkcffRSbzYZGY9dVdurUiaeeeiriNqMhlycHDhzghhtuiEpbmzZtcrkOtta2YtHmidBWLNqUtuLfpq+2KjqMoqzL6Zx/6wJyD3ydMHIlUpsnQluxaPNEaCsWbZ4IbcWizROhrVi0eSK0FYs2T4S2YtHmidBWLNpMpLZWrVoVFTlaKqJoauE0RbkTKePGjWPcuHExUQ4JgiAI7pR3GIM1KYM2ez+LtyiCIAiCIAiCEBRxnRMEQRCEBKa8yziqCobEWwxBEARBEARBCAlRNAmCIAiCIAghoTr+EwRBEARB8IcomgRBEARBEISgqIqW/WPuoLzz2HiLIgiCIAhCAiOKJkEQBEEQBCEoNo09tGdVwdA4SyIIgiAIQiIjiqYEYc6cOXzyySde+6+44gp+/fXXZpdn2bJlHDt2rNn7FQRBEARBEARBEASh5SKKJsELq9XKu+++S1FRUbxFEQRBEARBEARBEAShBaGLtwAx5+M5cHRzdNtsNxDOXRC02NSpUzl48CBGo5HZs2cza9Ys0tPTmT17Nh988AEpKSm8//77FBQUuNV74oknOHLkCPPnz3fb/+233/Lkk09iNpvp3Lkz8+fPJy0tzavfr7/+mmXLlvHvf/8bgDVr1rB48WKee+45fvjhB55//nmvNs444wwuvPBCvvvuO2bMmMHWrVu54447SE5O5s033yQ5OTny6yUIgiAIgiAIgiAIwgmBWDTFkEWLFrF+/XrWrVvHwoULKSkpoaamhjFjxvDzzz8zfvx4XnjhBbc6Dz/8MCUlJfzzn/9Eo2m4PWVlZTz33HMsXryYZcuWMWDAAF5++WWf/Z566qn8/PPP1NbWAvDxxx9z3nnnUVZWxosvvui3jaSkJJYsWcKUKVPo378/Dz/8MO+9954omQQhRLL1Znpn1MRbDEEQhKjRO6OGbL053mIIgiAIgtCCaP0WTSFYHsWKhQsX8u677wJw8OBBdu7cicFgYNKkSQAMHz6czz77zFX+2WefZdCgQdx///1ebW3atIldu3Zx2WWXAWA2mxkyZIjPfnU6HWPHjuWrr77inHPOYfXq1dx+++389NNP7N27128b5557bjROWxBOWF4YsYP8JDMTVjU9UG6mzgJApaX1v6ajwWltyknvpFLt49i8/nvokV7H5Wv6N7tckdA2qZ7x+eW8U9g23qIEpG1SPW+fspU7fu7BT2WZ8RZHiBH/Gf4bxSYdF/0wMN6iCIIgCILQQpAZTIxYtWoVn3/+OT/88AOpqalMmDABo9GIXq9HURQAtFotFovFVWfgwIFs3bqV8vJysrOz3dpTVZVTTz2Vxx57LKT+zzvvPJYsWUJWVhYDBgwgPT0dVVUZPXo0TzzxhM86qampkZ2sIAgA5CdFb9V/+Vi7y280lFYnAg8O3AsDYcIq72Pj8yuaXZ6m8K9Bu+mWZmRVUTbF9YZ4i+OX/pl2671z25eIoqmVk5dkCV5IEARBEATBgbjOxYiKigpycnJITU1l+/bt/Pjjj0HrjB07llmzZnHddddRXe2+Lj9kyBA2btzI/v37Aairq2Pv3r1+2xo1ahRbt25l6dKlnHfeea42fv7555DaSEtLo6ZGXIAEQTixaJ9sYlb3Q4AaNxnStFYAHGsSgiAIgtAiOK1NOWcVlMZbDEEQEgBRNMWIiRMnYrFYGDRoEHfffTdjxowJud4ll1zCjTfeiNFodO3Pzc3ln//8J7fddhtTpkzh0ksvDaho0mq1TJgwgW+++YYJEya42pg7d25IbUybNo25c+cydepUNzkEQRBaM/P67+WyLkX0yQ1USjRA0UJBJcWhWBMa0Cs29Iot3mIIgiCExYMD93JX3/3xFiNhOLddCTkS4044QRHXuRiRlJTExx9/7LW/saXSRRddxEUXXQTAggUNsaQuvPBCLrzwQgBee+01V1DvMWPG8M4774Qswz333MM999zjtm/UqFE+2/jyyy/dts855xzOOeeckPsSBEFoDeg0ASyZ4mfk1Gq5uechLux0nLNWD8asytqXk0/H/0ydVcP53w6OtyiCIAgJz+n5ZXROMfH6gXbxFsVFQVI9d/Y5wOaKNP6ysVe8xRGEZkcUTYIgCIIgxIVz2pUAYNComMWwyYVGgTSdWDQJgiCEwn399wEklKJJp7G/w3P0EuNOODERRVML5+abb6awsNBt32233ca4cePiJJEgCIIgJB6qosGU3p7kqkPxFqXFI8Z9giAIgiAEQhRNLZynnnoq3iIIgiAIQlzokVbL3poUbCHEzSrrPJ7KDiNp/8srJNUWNYN0giAIgiAIJyYSEEEQBEEQhBZHj7RaXhq5gyu7Hg2pfH1qHgA2fWosxRIEQRAEQTjhEUWTIAiCICQQiiS1C4n8JHsmn94ZtXGWRHBi0xoo73gqqmRmFARBEIQTGlE0CYIgCIIQNqIQEzwp7XI65Z1Po7aNZFgSBEEQhBMZUTQJgiAIggPRncQHRTnRwkur/C6/DG0LP+9cg5lXRv5Ku2QTAKpGb/9X0cZTLIcMKtZ0SWUoCELioUHlqaG/MSq3Mt6iCELMEEVTgjBnzhw++eQTr/1XXHEFv/76a9jtnXHGGZSVlUUsz+bNm3nggQfCqrNmzRquu+66gGW2bdvG6tWrI5bLF8OGDYtqe4IgCEJz0XTVXktUDp7Ztox7++/j4k4tOyj5WQWlnJRmYmqH4niL4kXd0DrKLy4XZZMgCAlHht7CgKwa/l+f/fEWRRBihiiaBC8sFgsDBw7kH//4R9TbjkTRZLFYoi6HIAiCLzztS3SKrcVbnYSOyjunbObcdiXN2mfz14w/2Qb7d62NwRxnSVov5vb2a2tLscVZEkEQTkRa4iKIIEQTXbwFiDX/Wvsvtpduj2qbfXL7cOeoO4OWmzp1KgcPHsRoNDJ79mxmzZpFeno6s2fP5oMPPiAlJYX333+fgoICt3pPPPEER44cYf78+W77v/32W5588knMZjOdO3dm/vz5pKWl+e3/tddeY9WqVZjNZp544gm6d+9ORUUFd9xxBwcPHiQlJYV58+bRu3dvnnzySYqKijh06BA5OTlccsklLFq0iP/85z/MmjWLoiL7qmthYSF33XUX06ZNC3juv/zyC/Pnz8dkMpGUlMT8+fPp1KkTTz75JEajkQ0bNjBr1iwmTJjAAw88wPbt21FVlZtvvpnf//73LFu2jNWrV2Mymairq+OZZ57hpptuorKyErPZzK233srvf//7oPdAEAShKXx++s8cqE3iyrX94i1KzNEAeUkWbu99gI+PtmnWvlVVhuSCIAhC66MlL4oIQlMQi6YYsmjRItavX8+6detYuHAhJSUl1NTUMGbMGH7++WfGjx/PCy+84Fbn4YcfpqSkhH/+859oNA23p6ysjOeee47FixezbNkyBgwYwMsvvxyw/5ycHJYtW8bMmTNZtGgRAM899xx9+/Zl+fLl/N///R933tmgMNu6dSvPPPMMjz76qFs7zz//PO+99x4PPPAAHTp04Mwzzwx67t27d+f111/n3Xff5ZZbbuHxxx/HYDDwl7/8hXPPPZf33nuP8847j+eee44xY8bw+uuv88orr/DQQw9RW2vPILRp0yYWLFjAK6+8QlJSEk899RTLli3j1Vdf5V//+heqKq9uITLapTX/s9MxxciqCRvpn1nd7H0LTaNLqineIgiCIAiCIAhCi6HVWzSFYnkUKxYuXMi7774LwMGDB9m5cycGg4FJkyYBMHz4cD777DNX+WeffZZBgwZx//33e7W1adMmdu3axWWXXQaA2WxmyJAhAfs/++yzAejfv7+rn02bNvHUU08BMGbMGMrLy6mqqgLscZ2Sk5N9tlVWVsadd97J448/TkZGRtBzr6qqYs6cOezfb/c99uf+9t133/HVV1/x4osvotFoqK+v58iRIwCceuqpZGdnA6CqKo899hjr1q1Do9Fw7NgxiouLyc/PDyqLIDRmaHYVj/8Z7t1axurjOc3W78gc++/szIIytlamN1u/giAIicJZBaVc/XuVl5vTO1MQBEEQhGan1Sua4sWqVav4/PPP+eGHH0hNTWXChAkYjUb0ej2KIye0Vqt1U8AMHDiQrVu3Ul5e7lKwOFFVlVNPPZXHHnssZBkMBgMAGo3G1Y8vKyCnPCkpKT7bsVqt/PWvf+XGG2+kV6/QUhY/8cQTjBo1iqeeeorCwkKuvPJKn+VUVeWJJ56gXbt2pKamuvb//PPPbvKsWLGCsrIy/ve//6HX6znjjDMwmcTKQAifk9PtFnP9MmuaVdEkCKHSUow1W4qcseKrmSo9cn9hyneD4i1Ki+GuvvbFp5dXxVcOQRAEQRBii7jOxYiKigpycnJITU1l+/bt/Pjjj0HrjB07llmzZnHddddRXe3uXjNkyBA2btzoshCqq6tj7969Ycs1bNgwVqxYAdizxOXk5JCeHti64tFHH6VXr16cf/75IfdTXV3tij3ltOoCSEtLo6amxrU9duxYXn/9dZcCzF+GverqanJzc9Hr9fz4448cPnw4ZFkEQRCiQ/NoVpQ4hSuKV78tlaEFkKk/UTOaRedhKel2FsXdzo5KW4IgCLGgov1Ijva9JN5iCEKLQxRNMWLixIlYLBYGDRrE3XffzZgxY0Kud8kll3DjjTdiNBpd+3Nzc/nnP//JbbfdxpQpU7j00ksjUjRdd911bNmyhSlTpvDYY4+xYMGCoHUWLVrE999/z9SpU5k6dSpffvll0DrXXnstjz32GDNnzsRma8j4Mnr0aHbv3s3UqVP56KOPuPHGG7FYLFx66aVMnjyZJ554wmd7kydPZsuWLVx44YV88MEHdO/ePfSTFoQEQubyguCN0oyZ/U5wQ6wmoXhcvaa+z6oKhlBdMLiJrQiCIMSOspMmYMw6Kd5iCEKLQ1znYkRSUhIff/yx1/7GlkoXXXQRF110EYCbwufCCy/kwgsvBOyZ45zBsceMGcM777wTUv+NlUEDBw7ktddeAyArK4tnnnnGq/xf/vIXt+3Ro0czevRoALZvDy1rX+M6Q4cO5dNPP3Udmz17NgDZ2dle5zBv3jxqa2vdXOemT5/O9OnTXds5OTm89dZbPvvdsGFDSPIJQmMinSAV9ZqKoaaI7EPfh1VPJrctg8DPhagJGxMdC6gT85q2trP24ZQfBykEQRASD19vQ3lDCicCYtEkCMIJhdrEz3tt7smUdz4tStIIiYooBlsOnlY2LYmWK7kgCILQVOQbILRmxKKphXPzzTdTWFjotu+2225j3LhxMevzm2++4dFHH8Vms6HR2HWVnTp1cmWzEwRBaOnIamNzEfkwWwbozU9TFfVxoQWKLAhC60G+VcKJiiiaWjjxUO6MGzeOcePGebm7CYI/TGkF1GV1I/tw8KD4rR2Z8yQ2MiCMD6oqv4xoMaaDSse0WnbXxPL7LL8UQRAEQRD8I65zgiDEnCMDr6S8S+ys7ITYMjirCk2EE8uSrmdyrNfU6AokxBRVdAgtmk8ugZdG7mjWPhNaTSjPsyAIgiA0O6JoEgQh6tzbby9PD23eiU5LoaXNeYbnVPLE0F3M7HIsovpV7YZSl3tylKUSBCERaGnvM0EQBEEQmgdxnRMEIer8rm2546+EXucWQiDPYAagc6opzpIIrRlFOTFVFvKGFARBOHGRb4DQmomaRZOiKFpFUTYqivKBYztXUZTPFEXZ6fg3p1HZvyuKsktRlB2KopzTaP9wRVE2O44tVJToJE8WBEFIFOSlJoTKieHCdmL+IlrLrT0x754gCELTaC3fAEEIRDRd52YD2xptzwG+UFX1ZOALxzaKovQDZgD9gYnAM4qiaB11ngVmASc7/psYRfmalfT09Ki2d8UVV7B58+aI6x87doxbbrkl7HoPPfQQkyZN4qGHHgq77rBhw8KuIwjNRXNPkGRQ0TI4kSfOijylQoTIkyMIgiAIQmOi4jqnKEon4HzgQeCvjt0XABMcf78CrALudOx/U1VVE7BXUZRdwChFUfYBmaqq/uBo81VgKvBxNGQ8kbFYLBQUFLBw4UJqa2vDqvvWW2/xww8/YDAYYiSdIDQvMiESQkGeEyGWnMgKTUEQBEEQWj/RitH0b+BvQEajfQWqqh4BUFX1iKIobR37OwKNc5wXOvaZHX977m8SR+fPx7Rte1ObcSOpbx/a/b//F3L5hx9+mLfffhuTycS0adO47777ALj//vt544036Ny5MwaDgf79+3Pttdf6befTTz9l3rx5VFZW8uCDDzJixAhMJhNz585ly5Yt6HQ67rzzTsaMGcOyZctYvXo1JpOJuro6HnzwQW644Qbeeust/vGPf7BlyxbAbul0+eWXc/PNN3v1d8MNN1BXV8cll1zCrFmzePHFF1m2bBnbt29n6tSpfPjhh/To0YOzzjqL5cuXU1JSwm233YbVamXcOMkwJgiJyPCcSh4dvJuLvu9Pcb0okBOZ5nYeV8NWfySGOi7s65RAXvmJcQUjIXGuoSAIQiIS6C0pb1DhRKDJiiZFUSYBRaqqrlcUZUIoVXzsUwPs99XnLOwudnTp0iU0QePEypUr2blzJ2vXrkVVVaZMmcLXX39Namoq//vf/9i4cSMWi4WBAwfSv3//gG1ZLBaWLl3K6tWrefrpp1m8eDFvvPEGACtWrGDPnj1ce+21fPLJJwBs2rSJ999/n+zsbAoLG3R4DzzwAACHDh3iT3/6E9OmTfPZ37PPPsuwYcN47733AHjmmWeorq5m3bp1DBgwgI0bN5KcnExubi4pKSk8+OCDzJw5k6lTp7rkEoREJV5zzXgHPZ7SoRiAfpm1fF0cuqJJ3KoEfzQtllRTKof3I5ZnuOm0yGsoMzpBEOJIoLdmC3yjCkLIRMOi6TRgiqIo5wHJQKaiKK8DxxRFae+wZmoPFDnKFwKdG9XvBBx27O/kY78Xqqo+DzwPMGLEiIC/0XAsj2LBypUrWblyJUOHDgWgurqanTt3UlVVxQUXXEBKSgoAEyZMCNrW2WefDUD//v05dOgQABs2bOAPf/gDAN27d6dDhw7s27cPgFNPPZXs7GyfbZlMJm699VbuvvtuOnYMzXBs6NChbNiwgXXr1nHdddfx1VdfYTAYGDFihEuWhQsXAnDBBRfw6KOPhtSucOLRLa2OUbmVvHWwIN6iCEE4UQdBvuemJ+rVCEw0lLaqKtqA1oH8RgRBEARBiEIwcFVV/66qaidVVbtiD/L9paqqfwCWA1c5il0FvO/4ezkwQ1GUJEVRumEP+r3W4WZXpSjKGEe2uSsb1WmxqKrK3//+dzZt2sSmTZvYtWsX1157LWoES8B6vR4ArVaLxWJxte8PpxLLF/feey9nnXUWp556asj9Dx8+nHXr1nH48GF+//vf89tvv7F+/XqXoglAEgUKofDC8O3c0MOnHrnZiFdGr5Y2oW5Z0jadE3maHL61yon2dAjBkWdCEARBEIToZp3zZAFwlqIoO4GzHNuoqroVeBv4FfgEuElVVaujzg3Ai8AuYDetIBD4Oeecw6JFi6iurgbs7mpFRUWMHTuWFStWYDQaqa6uZvXq1RG1P2LECFasWAHA3r17OXLkCN26dQtY54033qCmpoZZs2ZF1NdJJ52ERqMhKyuLr7/+2pVdbtiwYXz00UcALpkEwRe6WL55hJgQfvweoaUSj3sdb5dSQRAEQRAEIXpEKxg4AKqqrsKeXQ5VVUuA3/sp9yD2DHWe+9cBA6IpU7w5++yz2bZtG6eccgoA6enpvP7664wcOZIpU6YwePBgTjrpJAYMGEBGRkaQ1ry57LLLuPfee5k8eTI6nY758+cHzRC3aNEidDodU6dOBWDGjBnMmDEjaF+dOtk9G50WTEOGDOH48eNkZWUBcNddd3Hbbbfx6quvutz8BCFROVFjNLVWLCZNs1zbTGpi3oeTeFndNS+iwBQEQRAEQWhtRFXRJDTgtGACmD17NrNnz/Yqc/vttzN37lxqa2sZNWoU11xzjd/2XnvtNdffOTk5fPnllwAkJSWxYMECr/LTp09n+vTpru1OnTqxYsUKamtr+eKLL0I+jw0bNrhtf/XVV66/r732Wv7yl7+49fHWW2+5tsO1mBKE1o1MqGPJznfbgaJCDMN+DU85zIvJf+bm1ClsiV03Xii68mbsrfUiVnknKCeEwlYQhEQllC/PuLxyxueX8+C2rrEWJyEZ10lF1VmotohqojUhDixxZNasWQwZMoRhw4Zx9tlnB806JwiCEC3Ctf5pEdmmohD/KlALA1PsOS2GJR9qcj/hkH7yAtJP9l5QECIkAUzFWsTvSRAEQYiYcN7y9w/Yy1kFZTGTJZFJ0VpZcRE8MGBvvEURooyoDePIkiVLXH/v2LEDgHnz5nlZEV166aXMnDkzZnLs2LGDO++8022fwWDg7bffjlmfgiC0DE7U6bCv827t9jCt/fwSkRP19+WPTilG8pLMbCoPP5SAIAiC0PLQOxY+u6XVxVkSIdqIoinBuOeee7z21dbWxrTP3r17895778W0D0EQEouWlv0ukWjtLlii/BDixeujtwEwYdXQOEsiCILQNAKNFFr3KCI8JGF560Vc5wRBEJqBBPDWcUOCkgueuJ4IJdShQTSeocjbSLTfVCTI+FoQBKF1E+hT1Qo+Y4LgF1E0CYKQ8PylZyHndGvZn+PEWbEJT5CEETsBaP1xdex3O1yLrdag8GluWrtVXEIhl1oQhARFXk8g6rbWiyiaBEFIeC7sdJy3Loi3FK2FyD7oJ9rEONDZimIlmgR+rowZnahu0zfKXZ5Yz7IgCIKQWMgwwhsJ6dD6kBhNgiAIguAgEQZ/8bKcSkT9y9H+9kQY6SXbmtxW67dISwTkGguCIAihk4BDDyFKiEVTjEhPT4+3CG58/vnn7Nq1K95iCELciZc1SuJYwUT2SZdJemPs17A+pQ3lHcfErJf4WZG1rGFfy5K2dSHXXhAEQRAEX4ii6QTAYrGIokkQhIgQ9VIDnsq2o/1nUt55HDaN3mf5gVnV9MusiVp/zYfc9VjT2hS3retsBEEQBEFoKq3ede6bt3+j+GB1VNvM65zOuEt6hVz+4Ycf5u2338ZkMjFt2jTuu+8+AO6//37eeOMNOnfujMFgoH///lx77bVe9ffs2cN9993H0qVLASgsLOTGG29k+fLlbNmyhQULFlBbW0tOTg7//Oc/adu2LVdccQVDhw5lw4YNjB07lq+++oqffvqJZ555hqeeeoouXbpE52IIgtAs/LHrYY4ak/joaJsmtiRTwkCEYqHhtDRSFW3Ack8O3QnAkq/DDK4tdiJh0ZKf6JYsuyAIgiBEC/ketj5avaIp3qxcuZKdO3eydu1aVFVlypQpfP3116SmpvK///2PjRs3YrFYGDhwIP379/fZRvfu3TGbzRw8eJDOnTvz8ccfc+6552I2m3nggQd45plnyM3N5aOPPuLf//438+fPB6CqqorXX38dgH379jFhwgTGjx9Pampqs52/ICQqLc0p6cquxwCioGgSQkEGPILQSpAfsyAIQsIiS2utl1avaArH8igWrFy5kpUrVzJ06FAAqqur2blzJ1VVVVxwwQWkpKQAMGHChIDtTJw4kY8//phZs2bx8ccf89hjj7F371527tzJH//4RwCsViv5+fmuOueee25sTkoQhLBJnLmOfNIjpfVfucie0ngHEW/99yWx+L1mPZtwt4qWeyAIguBOoG+jvDO9SZxxshAtWr2iKd6oqsrf//53rrvuOrf9jz/+eFjtnHfeedx6662cddZZAHTt2pUdO3bQs2dP3nrrLZ91nEosQRC8CfWDdlZBKVVmLT+WZjWpv5Y6qGipcgtNIdS73vSnoylB8sOt2kFbxkW6/7K4hQ99svVm3jttC4/91pnlh/Oate8UrZWXDI/ys7Yrs8mRiYEgCEITkHdo/BerhNghwcBjzDnnnMOiRYuorrbHiTp06BBFRUWMHTuWFStWYDQaqa6uZvXq1QHb6dKlCxqNhmeffZbzzjsPgG7dulFWVsbGjRsBMJvN7Ny502f9tLQ0amoiD0orCLFEr9jQKzbXtkFjQ9doO57c1Xc/CwbtiVp7MqgQWg9Nf5qbc4C5MPu/3KBbQY/kyubrNAa0T64HYGK7kmbvW6vY73lXTVGz9x0xMokRBOEEZ0ZflTYGc7zFEE4wRNEUY84++2wuu+wyTjnlFAYOHMhFF11EVVUVI0eOZMqUKQwePJjp06czYMAAMjIyArZ17rnnsnz5ciZOnAiAwWDgiSee4NFHH+WCCy5g2rRpLqWTJ+effz6LFi1i5syZHDhwIOrnKQhN4cNxv7Bi7C+u7ZXjf+aFETti2mdLi9EUL0Qx5k2sr0lLy0jWFKuk5kSPNd4ixISBWdX8pWdhvMUQBEEQAqCioCrNP/VO11l47hx4eJBkHxeal5ZtP57AOC2YAGbPns3s2bO9ytx+++3MnTuX2tpaRo0axTXXXBOwzWuvvdYrK13fvn1dAb8b89prr7ltDxs2jA8//JDa2loJBi4kHAaN90y1W5oxJn1JwnghFHwpBJvbvLu5s8+1NCVouKhmqDqW3Gz9/XvITtJ1Fv60rm9M+3FmN3xyV6eY9gPez0hrf2YEQRCixaHB12JJyaHrjw83a79OS9Rcg6VZ+xUEUTTFkVmzZvHrr79iNBo577zz/GadEwRBEJqHUBSCrV1p2NLOL1Rlh/qTQuGRXHL61nMkphLZGZJd7bb9l56FHDfpefNgQTP0Hls8laCez4wqGihBEAQ3LCk58RZBEJoVUTTFkSVLlrj+3rHD7iY0b948NmzY4Fbu0ksvZebMmc0qmyC0dlqae5LQPMj8uPWi1tjvrs4Wn/hvF3Y6DsCbBwsk+KkgCEIrx9Ot/NukW0iinovpER+BoogtyYYlz4LhkCHeoggJjCiaEox77rnHa19tbW0cJBGE1klT3ZFe1f+TLWo33o6wvswvWwa+1JCtQTl5VkEpP5ZkUmVpPZ//cO+K/AabTmv4LQiCIDQnnZTieIsQNSrPqcTaxkruK7kotuh8VVtKvEchdCQYuCAIJxRNnSCN127mRt3ysOu19O+nTM4baO7YSdGic4qRu/ru5+5+++ItygmPDKgFQRCEljmaAGuWI7lGFE6gpV4DITiiaBIE4YTkRA20LBPcExeDxu4ylusnxXE8rFSi8Tz6+21pFZXLuxx1nbeT+DjOtS5aqrJVEAShuRAX6chI11lI10ng8tZA67GdFwRBCIN4uX7EW88T6cAn3nInIjlU0V9ziP3xFiTKtBQlQrBncmK7Ev7c/QgpWhtrNwQpLEQVRV4YgiAIQZFXpTcfjN0MwIRVQ+MsidBUxKJJEIQTinhPop29T8gvY9WEjbRNqo+rPLFmgmYTkzXfx1uMqOCpnHw5+VFeMyzwspgRQieWK77JjvuSrPV9f7ql1TGj87HYCRAlUrRWVk3YyEWdiuItCgoqu4ynUmfNircooSMzOUEQEpaWsbDTHMiruvUhiqYYkZ6eHtX2rrjiCjZv3hxx/WPHjnHLLbeEVaewsJDJkycHLbNixYqI5fLFGWecQVlZWVTbFIR44/kBPaddKQDd0+qaX5hm5GXDQzxpeCreYkQVp7Kyp+YwAJpWNjwK1dqv5Q6P7ZL/Z/gOru9xOM6yBCdHb3chmNbxeJwlAa1i4NPyO/iy/G/xFiV0Wu6DKghCK8emS4m3CHFHkku0XkTRdAJgsVgoKChg4cKFUW/70KFDfPDBB2HVsVqtUZdDEITQkBhNgTHasvih6g9EMjud1vE44/LKoy5TtGl8Zj3Ta8lPbdpDEW8rwfCxn69B07J/DPG46opiHzbWWvPi0LsgCEJi0z1bpX2yKeTyakv7fMaQljeWEILR6mM0ffXy8xTt3xPVNtue1J3fXT0r5PIPP/wwb7/9NiaTiWnTpnHfffcBcP/99/PGG2/QuXNnDAYD/fv359prr/Xbzqeffsq8efOorKzkwQcfZMSIEZhMJubOncuWLVvQ6XTceeedjBkzhmXLlrF69WpMJhN1dXU8+OCD3HDDDbz11lv84x//YMuWLYDd0unyyy/n5ptvDngOhYWF3HnnndTV2a0v/vGPf9CnTx8ee+wxdu/ezdSpU5k6dSpXXHEFjz76KGvXrqW+vp7LLruMGTNmsGbNGp5++mny8/PZvn07H374ITfddBNHjhyhvr6eSy+9lCuuuCLkayoITeVEDdIY6XmHWq1tUj2n5VXw7qH8yDqKM5uNf6bIOpz2uT9THqSs5zWZfXIhkLhxBXwN4l4csYOyATDtx5b7m1CCBARKxNOK5Fonwnk4n6GWraITBEGIDRuuBvg1YccBiUhLHXsIwWn1iqZ4s3LlSnbu3MnatWtRVZUpU6bw9ddfk5qayv/+9z82btyIxWJh4MCB9O/fP2BbFouFpUuXsnr1ap5++mkWL17MG2+8AcCKFSvYs2cP1157LZ988gkAmzZt4v333yc7O5vCwkJXOw888ABgt0b605/+xLRp04KeR5s2bVi0aBFJSUns27eP2267jddee42//vWvLFq0iP/85z8AvPXWW2RkZPDOO+9QX1/PzJkzGTt2LACbN29mxYoVdOrUCYAHH3yQ7OxsjEYjF154IZMmTSInJyfMKywILYuW9kENd4XpoUG76ZpmZFVRNmVmfYykih02DAAordDg1595ek6y+3a497wpZu+mEj21x1Ijrh8WCakdCX6t/Ykdj9NRguTsS1bMjNOsZV0zySMIgiAIQmLS6hVN4VgexYKVK1eycuVKhg61a7arq6vZuXMnVVVVXHDBBaSk2H1zJ0yYELSts88+G4D+/ftz6NAhADZs2MAf/vAHALp3706HDh3Yt28fAKeeeirZ2dk+2zKZTNx6663cfffddOzYMWjfFouF+++/n23btqHVal19ePLdd9+xY8cOPv30UwCqqqrYt28fer2egQMHupRMAK+99hqff/45YLes2r9/vyiaBCHGxNp1LsORkralKdQ88WXO7rkrIfUWIRAtuaPRTtFXufY/OgUuF1CORjerW1ode2vcY154y9nCH864Evja/SPnU6YZfuEPKedSGLBkaKyasJF3CvN5alcTHhBBEARBEJqdVq9oijeqqvL3v/+d6667zm3/448/HnZber3dOkCr1WKxWFzt+8OpxPLFvffey1lnncWpp54aUt8vv/wybdq04f3338dmszF48GCf5VRV5R//+Afjxo1z279mzRo3edasWcMPP/zAm2++SUpKCpdffjkmU+g+zYIgREa4CiAJ0uiN87Ur6orE4tQ2FcwfuIf7fz2JL4pyw6ipInczOnTUlgOQrjVHrc2LOh0XRZMgCIIgtDBan29AgnHOOeewaNEiqqurAbu7WlFREWPHjmXFihUYjUaqq6tZvXp1RO2PGDHClfVt7969HDlyhG7dugWs88Ybb1BTU8OsWaFbe1VXV5Ofn49Go+H99993BfROS0ujpqbGVW7s2LG8+eabmM1ml0y1tbU+28vMzCQlJYU9e/Y0KaOeIJyIDC04lzNO+mOz9XfCqZt8nLA/VUQiX5uJ7Uo4v31xiKVbprLFGaOpqyODY/c0o/vxZpeoeYjPeQV+2hvcLhP5VyEIwonG6NwK/tDlaER18ymjm3IkqvK0dKtvQQgFsWiKMWeffTbbtm3jlFNOASA9PZ3XX3+dkSNHMmXKFAYPHsxJJ53EgAEDyMjICLv9yy67jHvvvZfJkyej0+mYP38+BoMhYJ1Fixah0+mYOnUqADNmzGDGjBkB68ycOZNbbrmFTz/9lFGjRpGaao+p0bt3b3Q6HRdccAHTpk3jyiuv5NChQ0yfPh2AnJwcnn76aa/2xo0bx5tvvsmUKVPo1q0bAwcODPvcBeFEpmfOyIjqNVfWuRY/hgpwAi0pM8qcPgcA+PBIQ5awliN9ZDgVT61V1RHX8wrSeWu95oIgtGz+NcieGOr1A+3CrvtT8k0ATCCyAN+Bx13N90UOljgj3khW5NaHKJpihNOCCWD27NnMnj3bq8ztt9/O3Llzqa2tZdSoUVxzzTV+23vttddcf+fk5PDll18CkJSUxIIFC7zKT58+3aXsAejUqRMrVqygtraWL774IqRzcNYB6Nq1K8uXL3cdu+2226itrUWv1/Pyyy+71fvrX//KX//6V7d9o0ePZvTo0a5tg8HACy+84Nqura11Ka+c5yacCIjLSktB7lLzuRHG6lqHqiBrSYq0EwnF499EwN/EJVQZNUlHUFUZigqCIJyoJNI3TYgu8nWPI7NmzeLXX3/FaDRy3nnnBc06JwiC0NxEqnRo8QtTAU6guc4t2v20hHhbKVorWXoLR41JPo8XJNVTY9VQbQk+fGkZg9fg9ySRss6F7joXGmndn7D/8XWbSAUSBEFIaBLFTU71leUkgUj8EYoQLqJoiiNLlixx/b1jxw4A5s2bx4YNG9zKXXrppcycOTNmcuzYsYM777zTbZ/BYODtt9+OWZ+CIAgJTRTGY4fXZGMxaoCypjcWRaI1mIvFkPXJoTvpmV7HhFW+XRTeOmUrx4x6Lv1xQMR9iMVW5Cg+/gpcThAEIbG5qFMRE/LLuXljr3iLckIi34vWiyiaEox77rnHa5+vYNrRpHfv3rz33nsx7UMQEoX4+4DHXQAhBHwOfMIcDVXsTY2GKAlPNBU3PdPrgpYpSA4to5nfX5rivRm/X2XrGmLL200QhJbGzT0PxbF3eWsKrRfJOicIgtAMJLrJcrRp8Wcb0HWuxZ+dT1rnWbVe4nu/Ak+OWoKbpiAIQryI/6KnIMQeUTQJgnBCkSi+8i2FcCeMLX3s5Bz8+dILJuLkWauoPDBgD73SQ7d8DfYTSLyzDI0TRZmbGPfHfq09r7lTCXti3AlBEAQhWiTGt02IJqJoEgShxbNqwkb+X599celbDVH9kOhpZYMRqvQtfYJprVUwGUppizbeooRExxQTY/MquKvvvqBlW6sllj/8nm0L/Sk6leRxvYvBrp3z+In1qAmCIJywWNpYKLmmBEuOJaL6LX18LPhHFE2CILQKzm4XWsDlaJorW3Up7B9zO5Xthkev0QQl3HljPIcNqVorE9uVRFT3eE4tlblbSM/KDFo21hZOMlePjJZ43dL1quOZDf2ZistvLMjFlemCIAitHb1ia3Ibrcm6vv6kevu/neub2FIruigCIIqmmFBeXs4zzzwTtfaWL1/OvHnzQio7Z84cPvnkkyb1N2PGjLDrvPrqq5x33nncfvvtYdc944wzKCtLrKxMghAKFkMGADX5/YOWTTS3nlClaYlWMH/tdZA5fQ7QN6Mm7Lr1eisAil4fbbFCJlaT9VAVYy3ljgf7TbUkpcfDv4M5fQ4wMCv8ZzaeyEq0IAiJyOCsKi7vcjTq7Y7IqeSz03+mX2bo7+rAC5wt5YsrCOEjiqYY4E/RZLVa4yBN6Djle/PNN8Ou+9///pfnn3+eRx55JNpiCUKLJV1nicrKV0sknkOnNgZ7VrIkbXSvvarq+LbyalDtSqhYKeHCaTUSqyp/NU6U4a7nfUuE826bZv83WRPb94VOsfHpuE2cVVAa034ivabH6k9mc825UZVFEIQTjyeG7uLP3Y9Evd0ROVUADMyqjnrbLZ4mfkwlQHrrQxdvAWJN+Yrd1B+O7gqhoUMa2ZN7+D0+Z84cdu/ezZAhQ9Dr9aSnp9O+fXs2bdrERx99xKRJk9iyZQsAjzzyCNXV1cycOZMrrriCwYMHs2bNGiorK3nwwQcZMWKEW9urVq3i2Wef5bnnniMnJ8dn/+vWrePll1+muLiY22+/nYkTJ6KqKg8//DCrV69Go9Fwww03cN5557FmzRqefvpp8vPz2b59Ox9++CHDhg1jw4YNLFy4kC+//BKA0tJSTjvtNP75z3969XfvvfdSWFjIDTfcwLRp01iyZAmfffYZVVVVjB49mldffZWRI0dy+eWXM3/+fDIzM7ntttsoKytj4MCBkd4GQWgSzTG5/GDsZn6tTOXGDb2bobfwiNX3vDWPE2y2Efxcew6K5gfgs5i7zoXTejhKr0QMBh6N32NLevZsGgMAFn1axG1Ecs0ydFaStCo39jjEZ8dyI+7b3/PW1GDg75Q+5PgrNCtuQRCEWPNT9SXsM44A/htvUVo1rcmdULDT6hVN8WDBggVs2bKFTZs2sWrVKs4//3y2bNlCt27d2LdvX8C6FouFpUuXsnr1ap5++mkWL17sOvbZZ5/x8ssv8/zzz5OVleW3jePHj7NkyRL27NnDjTfeyMSJE1m5ciXbt2/nzTffxGQycfHFF7uUWJs3b2bFihV06tTJrZ1bbrmFW265haqqKi6//HIuv/xyn/3dd999fPPNN7z66qvk5OTw/fffs2vXLgoLC+nfvz/r169n8ODBHD16lJNOOokHHniA4cOHc9NNN7Fq1SrefvvtEK+sIISCyh+7HmHFkTyOmww+jjYv/TLds4Elync0VnIkwvlZ9alANdakLCCaFiL2AOFqCwkU7knsLLBakoonMbAk27/h9WntAGNYd6YpVzv8WN2RZZ2UZ0IQhNbC2uqZXvtiNdZRY9h2zJHXvuBBq1c0BbI8ai5GjRpFt27dQip79tlnA9C/f38OHTrk2r927Vq2bt3KSy+9RHp6esA2zjzzTDQaDT179qS4uBiADRs2cP7556PVasnLy2PkyJFs2bKFtLQ0Bg4c6KVkcqKqKrfffjtXXXUVAwYMCOkcRowYwbp16ygsLGTWrFksXbqUkSNHuqyX1q1bx5NPPgnAhAkTAirNhNaNQvS/Sz3T67iy6zGG5VRz88ZeUW49chLl+9tccsTzfG26FADMydmALxehhumwTrGxaOR2ntrVkbWlLe9d1GIHpFHE8xokosWWPzxlCSxb0yV3WRxFGlvJUc2/IimBnshEutGCILQK5LUSgBi7zukVGxPalvPZsZymdyY0CxKjqRlIS2swjdfpdNhsDSvsRqPRrazeEYBWq9VisTSkiezUqRM1NTXs3bs3aH96H0Fs1QC/3pSUFL/HnnzySdq1a8eFF14YtF8nw4cPZ926dfzyyy+cfvrpVFZWsnbtWjc3QEXsI1s1qtX+XzxwvtT0MY510piWFDA7Uklb4k/W31vv3VO38OaYrQDkJZnpkmri/04ujLgff5emXp+BydA8yqt4Dn7j9fyrQLG5a8IF2o82nufXlLNVXYqiyFA0CsfbfU1Vqu/YJwk1CWvdj4UgJCTGMh21xfFLpiE0BZWbehTSLa0OsCt2evtLqhLF92uoTV3T7Qh39d3PaW0qote5EFNE0RQDMjIyqKqq8nmsoKCAoqIiSkpKMJlMfPDBByG12aFDBxYuXMicOXPYuXNn2DKNGDGCjz76CKvVSmlpKevWrQsaH+mrr77i+++/56677gqrr8GDB7Nx40Y0Gg1JSUn07duXt956y6VoGjFiBCtWrADg66+/pqJCXhitjR3L2rFjWbuotXdkXRZVhckhlW2JCpHWSCLfhhyDhXbJ5rDr+bXi8PPQfXvaAr47dX7Y/URCNK53S8sglqRrz1slj1NonRZWvUR8NgPJ5O++RHK3wned85BFY69ZnRY4m5O4zgnCicneT9uy//P8eIshRECewczFnY/z0JD9AMw+uZD/DP+Ndskm/5Wi+KoP1lSuwW6Aka5L7ORaQgOiaIoBbdq04bTTTmPAgAHccccdbsf0ej333HMPo0ePZtKkSfTp0yfkdrt3787DDz/MrbfeyoEDB8KS6ayzzqJ3797MmDGDq666ittvv538/MAfgsWLF1NUVMQll1zC1KlTWbhwYUh9GQwG2rdvz+DBgwG7hVNNTQ29etndmG666SZ++uknpk+fznfffUeHDh3COhch8VGtGlRr9F4v5bvSKPw28sC1QgORjglCzQZyIkwvE+kcw5El1Ml/S7HQ02nt1mLVqt013b8zV+DzjqdyuildR1LXpWgKo3JdiR5rvTPId+CKLeXZEQRBEHxj1dkXdntn2GOMxlqxE+5XQxaUWw6tPkZTvFiyZInfY84g243ZsWMHr732mms7JyfHlfFtypQppKamAtCvXz8+/PBDv20vWLDAbXvDhg2A3VXtb3/7GzfffLOrLYDRo0czevRon3VeffVVv/144pTVyRtvvOH6e/LkyUyePNnt3BYtWuTa/vvf/05trXvAZEFoKXSgmJKoBpxObLql1bF45HZu3nAyWyoDx4trCbTE8cq0jsdZX5bBgdrkJstfaumEjgCrlVGmPjkXU0Z7Mo5vjXlf/jOjJQBqwE33Yx6uc9GRP7RWFGDfZ/kk59aDd26FGBOCjLokyjuMJuvwWrGiEoQEw2awUTGlAn1R63OlC+Xbm2jvpHCtlptboZNYV0uIBmLRJAjCCUk0vp/5eiPfJ9/C7amhucAmAqGctybA5354jt0t+PT8csDuwz+xXQnOIUJiKW6aJ7detHsJNribfXIhLwzf7rYvlAGaL8XLf4uf5LXi5x0FVLa91Z6q35JClDR8Ss9Pon7S1177m2Kd49WW4myz5QxbY/m7OT2/jCy93eXApjotk8LDWBqulql53gQpoy+lvMt4atr0bpb+BEEIHXNHM7YMG5lnZcZblISlxVuBtnDxhdgiFk0tlOeee45PPvnEbd/EiRO5/vrrY9ZnWVkZ11xzjWvbZrOh0WhYvHgxOTk5MetXaD34S9t6uL5vlHuJ9Gh45OjrAThNvyOKrcaXdskm3hzzK/O3dWHlsTZexz3v39Vdj3L5SceosWj5pji7WWQMlVDGPy1HFeFOkjb6kisAqkL5plQo8D6erLFyY89DPLe7I7VWLagq5btTwX8+CS/0mVvCkul0zc90Vor4Nqxa7rgyrZH49zuQfJGM53MNZu7rv4+fy9OYvakhC2ekc4Ngcdc9ldRdU+s4s6CMF/e2b0KvAdDbXTxUjQxnBUFILBJNiRRq4oyQFmkS/WMqJATyZW6hXH/99TFVKvkiJyeH9957z7VdW1vr5oYnCJHybqkzYPK8uMrRHMR72BFsbNA11Z4J83dty90UTf6sbHIN9qDaTh/+E2PskThn2Zym7dM6FjOlQwmVZh0v7u1AuyqVI5uzyepoCV45Ql4x/AuACQyNWpued8+qSwVzXdTaD4kw7punvOHccr3DVaIgud69jRg/N06ZHxuyi1yDhaWFbakwhzfkjPe7UhAEwZOIkjAEeOHGw/K2jcHMmDYVfHgkr9n79oXEXGq9iOucIAgxJ5sqBim74y1GzAhl1SrcoUQ9OkwkXlyDRFGx1Kfms2/MHZjS/Gc3DEXWyMY3TguZRLkazYMzvoPzmukcock04Sfw8ybUaPPuEoVYSvXY9ug6jiqNpjyjIdWNcibBcK+U1tX/ifVbEQRBcGpQrIbEch1cMHA3d/Q+6Foo9CTelljju3TksZxsr/0RDROEuCIWTYIgxJy3DfPopTnE6VG0SghGqCbCTSFQLKOmMp+/OP5aFbM+fBFsZSlRFp5qs3vY/83tSVKNv1Trod+fQCuOLvzO+FvH6CdR7m3TUR3/V3zt9ibKyphIiGRFN7wqEapUm/hQqBHGhIoK8b+tgiC0UkJR4qsOew5Vo421OGGRbbBbIcdyDBsedjlStPbVqzKtlsXZmXgHbxBaGmLRJAhCzOmlORTV9nqm19I9zberSzwmNInyqY4HTXHrUVD5fdvSmFkGhSKLzVHKqk/zefwXevMrPZveUQtEg8pzw3YwOrfC5/F4n7bq8dx46vv8PVeh5og8Ob2WrqnN7FLnA8+zaMqvpen3LLwWmirrzrpTqbS0DbmOJc9CyTUlmPOjYWYnCEI0UFvJYkxLJduRDMKpYIrKmKvxpyBAc/0ya1g8chtJmuBf3lxD7NzwhfggiqYYUF5ezjPPPBO19pYvX868ec0Tu2bNmjVs2LChWfoShEh5ccQOFo3cHrxgjIm2W0oiE4tx4uQOJdzdbz8XdChuQiuBJr7BJ8WKK0WZx+fQsXsZ5/E2kx27mud+x2tMrgLFbQZg0tvN/DP0Fvpk1vL3PgeCV4wDSohKj0jdAF4YsYOXR8X/PRMLmusZc3YTqYJrZcUdvF3yaMjl6zvaY1GZO4miSRAEAWBsnu/FIn/fxoaxTohBwQO84G/qUUi3NCM90v0v2sR70UqIHaJoigH+FE1WqzUO0oSOxWJh7dq1bNy4Md6iCCcIreXjEm9/9khoqsT+LJnCmb/m6O2TweyIVrFCMVsPpzUl5EpeRVpJJEsNCr8MvIGNQ/4v3qL4pdh8EjXWbI+9gW9aYsfSCv3ZieZTFpKraBRpSncmNT3A0QD3tnX8LAWh5dJCfoP1wC59osTETLyLFo2FiWh+hxPvCgn+kBhNMWDOnDns3r2bIUOGoNfrSU9Pp3379mzatImPPvqISZMmsWWLPcXzI488QnV1NTNnzuSKK65g8ODBrFmzhsrKSh588EFGjBjh1vaqVat49tlnee6558jJyXE7VlVVxQUXXMDnn3+ORqOhrq6Oc889l88++4wjR44wb948iouLSUtL4/7776d79+7MmTOHrKwstm3bRlZWFhs3bkSj0bBixQr+8Y9/ePUvCKFQl5yMTRNfPbYtyMwmGtZI0fjYtRQdhT85W8cUPnjA6JZCitbKyel1/FIRaHLegFVjRHGsOTkthGpT8sPrNAoXSxNiG2+V/BudYgQeCtp9S1QAO1lW8gB1tixgUZPcU/2iie7wz19MvKZMUJr0bmy5t14QhGbmwbxclmWkk5NiQ1PX+m0wgr0e/X07j/c8n5TyfcAmzwphtxUJiTzeFHzT6hVNH3/8MUeP+gsUGxnt2rXj3HPP9Xt8wYIFbNmyhU2bNrFq1SrOP/98tmzZQrdu3di3b1/Ati0WC0uXLmX16tU8/fTTLF682HXss88+4+WXX+b5558nKyvLq25GRgZ9+vRh7dq1jBkzhq+++oqxY8ei1+u55557mDt3Lm3btmXnzp3cd999vPLKKwDs27ePxYsXo9VqefLJJ0lNTeXaa6+N7OIIArB86gX2P/7972bvW9XaV6UsyTl+SsgMBBLrgx3tOxKeJVPTibbFTOAJduC+7uq7n7F5FUz/fgCl9cFXaEvbrrX/8Wvo8sUbi5oc8Hhr+IUfMfd3/d2UrHPRpkGW8HpvDfdEEITWyYakJABUvQohhOUz2jLCaj+QsiXRFkS21/0Onc53XNWavH7U5PWDz9/wOnbFunn8on5BIVvc9idrE9ubR4gtrV7RlAiMGjWKbt26hVT27LPPBqB///4cOtTwQ1+7di1bt27lpZdeIj3d/0r1ueeey8cff8yYMWP46KOPmDlzJjU1NWzcuJFbb70Vm82GRqOhvr7eVWfixIlotYmVEUE4sbmsfQF5kbqaKvZnWY3yir0vIkrI7pkIK27xbSKtl0gqKt+EZ8kUSa3Ew/kc9XAEyTcECLwZ6pkGK2fTGoA6bNrkqGs9Imku0gF7PO58NCzowqnjfD7CteTUACW5/UivLgyrnqtfx7+xem/0SirjjaRbuEr7O9y+GIn/mhIEoQViVnryUtEdaLXPA5EbMgR7RdmSQ01bEV2+qLiFJKUS+HdY9dLMWZzCdJZ6KJp6phuBpkbTFFoqrV7RFMjyqLlIS2vIZqTT6bDZGl4eRqPRraze4SOs1WqxWBrilnTq1InCwkL27t3LwIED/fZ1xhln8Pjjj1NeXs7WrVsZM2YMdXV1ZGZm8t5771FbW0tqaqpbnZSUlCadnyBEm83J9tWlRE9t2pwfxy2155CmKQWalpUj1PlXMAWYlztPBK6IqiMAt02JjaI7JIkcJxpK3CUVqMrcQVJN4s5io+GKGapSwKaxK5pURdtCJvaOqKUJNKr1yijnwwUt1KxzNkWLRvWtoPdScIdxEX4edBMptUVw4Dm/Zfz9/p39xMpF+Ia2P9NJKWZcRiGrGs/5EugeC4LQerAqJwGganrQFEVTIEwnmag+o5qk4qSotBeqVazzfW1SMwPWC8YVJx3l2m5HOPvrwRG2EBh5vbccWr8jahzIyMigqqrK57GCggKKioooKSnBZDLxwQcfhNRmhw4dWLhwIXPmzGHnzp1+y6WlpTFw4EDmz5/PhAkT0Gq1pKen07FjRz755BPAnmZ0+3bfmXTS0tKoqakJSSZBSEhU5z/N9ylqjjn26srr+aj8/zW5HauazvdVV+J8/ds0Og4NugZjesew2omGJVB9ah4AxqyTIm4jEJFZNvmnJkWLMfUYxtSWnYLX3/Maaha3aLKn6/l8OeHpZu3T37tBr9jQK/FZRY4GTrfhYFRa8rGo4QW+rUttG6oU7lthvhxVrNg09cELCoIgxJVQvpeRjQ4tBfYxxhljYhOg3J/iP1qWp9M6HgcgTWtFtULVocAKs1AXIlrEepbghiiaYkCbNm047bTTGDBgAHfccYfbMWe8pNGjRzNp0iT69OkTcrvdu3fn4Ycf5tZbb+XAAf/pps8991yWL1/uZs31yCOP8M4773DppZcyadIkvvjiC591f/e73/H5558zdepU1q1bF7JsgpAwRHmufJi2HCbUSVbzYdXWUW8oD7veUfVyNtZMQ2+wx4CpT22LOTWPspMmRCSHipaNNRcQ2eckNoqNqAxGwpghN6d6JlBfWkVHvS01QAmotWZhU8O7Vyb0DB8+HLMhO3yhgrCv63mRV44a9hP4YOwvrBj7SzP3bH/OFCX4c9sUq6DGj/Nrxc+zqvyvEbUTqjLSU5kXqujlub9Q0vbHMKUKpwdBEJqdVqQhaM7odOd2b1J1F819+Ruf9ZHNWXxzcDjta5seq2l9vo6ZHQqiksxHaB5avetcvFiyZInfY7fccgu33HKL274dO3bw2muvubZzcnL48ssvAZgyZYrL3a1fv358+OGHAfueOHGil8VSp06dePHFF71c5xYsWOBWrlu3bixfvjxg+4JwIvE8lzv+WuW3TDyCOZbm/xRRPdXx2vc3YVSAXcZTsak7grRjp5Iz2V91MTrDMvDwzfdFps6C2U+Gqmjh2brVpGA1B1au+DMhb0mc0/1GVlRmc1PqNJ8uWIqiZ/Hxl+mk+xL41rsBP6d8RGlPRkYGldrBwLZGR8If7F3wg43LV9m4sUBFDUNz0j+zmoJkc0hlI71zSdrmG7x6Pl+/5KZwU7d8Ttltg/LAdaNhqXeofggQwbc+TBOlcK+oxeDbGtwfAR8hmYsIgtACydRZMAYvFjKhfzP8ZA8N86vqLK0CG7IHs67rcFK2r4ej4b3fPVl6sj0RyDB5ubcYRNEkCEIro/k+QL1S7R/NVCJ39YhV7JIQeva516DryqflfyFP8aOM8MCGIwOYElosgeVjN1Nar6P7+lDlbDq7P2qL1aSFdsHL+rsd2UntsKg9gf3RFC0kBu61oajgOw9MA2n67IDHNYrdDP+YZSS+7q0mUhVNGD+5S76xu6Zp8Yw2FriRp4fZXcafDhASw69LYELrDO3Cbcqzx0qsyFHhoO+STUkcEGlVr2sX5GJaUXkrIx3VaakVYb/2ujKZEAQhUYndh6Vdcj1Fzex0dE5BCee2K+U7U5gVA1wGFagzOOIA66LnBuhrIU1ITETR1EJ57rnnXDGXnEycOJHrr78+ThIJQmLRHBYpN3b+DYCTdUdp6a4bzimdRmMfFNSTG7O+cg0WonW9fN1nz+mp1RR+sHHP+fTvu/4JY1BVT3TYbzqV/5b+HzrNfMDC3W/alTM3tPOWK5pPndJ4GbIRsZzum5JKqE07gE2fBpbGFkvNp2RQmrW3SFB4+ui7DEp+DdjbrLKG+nw5B/5ftFF5Py+XXh0qYW/s5BIEoSWQ2G/WSIh27MfmIJS78Pe+B7CpGr49Ft17Vq2xK8zMYeiZOpf1QW9LpsLPqou4zrUcRNHUQrn++utFqSQIPvCldpjUvoSVx3Kpt2maZBUQvK/ApTfVTMbGGnfpmvl7aVVUqjJ+Q62OMBV8VEdOits/4eH/wsVicKdRGlYXI7llBUn1vDLqV65b35v9te6ZPg0auyKp3mbvY4fxfABS9XlgDC2rTSiZyRr2xW/46zk+rMrajqqxohhSoa6iccnI+2hUtcaajS3oDYv/oNXf2f69zz6y9QrrzLDFeCmwwE9J71ZSUlLQ6pp6bp6xlgK3V+twP7Ro7c90/K+sIAjxYkybSj5HS5vkeEsSTWL/Vmvcw7JTN/PYb535tjg75v0CFLf7hqS6/KDlNIANMCg26jyONVYEVRi6AGBLygJKQ5Lh/O03ALCUeSGVFxIXCQYuCEKrZnxeBbf3Psgfux6x71BSWVYyHws5TW7bc1Vlt3EMn5ff4rOsTj+C76r+SBlTmtxvUyhLr8eYehRrZobbfpcxi0bD8XZfU50UWjycxvTLrCFV25C1a3T7aew1jghQo3mmoRatFpPB0PSGnOJGYFV0en4ZyVqV89qXeB1bOf5nlp3aEN9KRcWqMUX98gSXM0LlTgTVmuXOazrw8vHF1CqnN3/fYZCmzybV2MPnsXPalTE8tzqidkePHk3P4b9z2xdzFaOqpUfxkKhc5ESxBhAEITLaJ9vDCqTGJnlaXAhnwSYaav5cg4WbexY2uR0nQ7Or0DrGrv4WOk0px73qeeJso43BHOBEFSzaLAA0SuBEJULrRCyaBEFoRtydVJRm8FlJ1dkzXWTp7RFhtPrRHDH3JUM5C4gku5F/PqycjVVXB3zjfdARw8hGfD+2GrN96JBs8UhD7vxDb1fIlKWZyDWYSdK4p3v3NzAxKCrPDPvNbV+XrIEstWTQh2zAW8ESFXyMhDxF/PSciVRnpsOqVU3oqOE6RPLIBrMES9c1ZGQ5ll5FecoalEPea0FeIXMSTm2SOCgae7bIeiVYdtfmV2k0vm/n9bgFjsCXXWf7KZ2491hFx+flt2DjYwBySybSo+wsNmT9lwp2xlc40VQJQnxx/AZVWs/PsTIJDE1PoBYSsbBCfnzIrqi1FS1kHNN6EUWTIAgxp5oUvuQ0NJoy1OB+LE1EbfT/QER/2FOWtx5V42cEovoxh2luHGI0JNjy73TV2MrGiafCxFla68dnvjb9ABsYSKCsfZEQzlWszkyPat+1BkvwQn4JLnm1wZ5vRqMN/SxD+1WpmPWVaC3R+g2q4XQeP1zmeok31Un0S6fxjgbutqVqh7LD+Dv0ujTgE3QWu6Wo3tY8vjJeE5REv6CCcIKg1+tRyQZMXi7TiUhOlYoaQiyDtZ21jN0XaqsRnLiPVanmCrEQrdimrk9uC7jvQmxpsuucoiidFUX5SlGUbYqibFUUZbZjf66iKJ8pirLT8W9Oozp/VxRll6IoOxRFOafR/uGKomx2HFuoKImdK0YQhND4jPFsYCBt89u67c8yJHNhrzui2leoxsy+vn8aVGZ1P0S23r/b2KEfsinelu6zL79KJh89ZuuboqyIBk1/va6asJFe6Z7e+c7W4zfCiMWHo/H5hHpmlZk7KG2zzm1frAZeXs+irzIaHeVtNlGWvSekNmL5AZbxp29ipQuL1vVuzlFZKH1ZbO154djrKGpa7AUSBCFkRo0aRZ1mEpD47/u2xfCfp6xcZqyMtygB0el0aLX+k5tc2LGIVRM2kqKNrslVoPsX6tJuIGSy33qJRowmC3Cbqqp9gTHATYqi9APmAF+oqnoy8IVjG8exGUB/YCLwjKIozl/Ns8As4GTHfxOjIF+zU15ezjPPPBO19pYvX868eaEFRJszZ45XNrpwmTFjRth1hg0bFvB4ZWUlS5YsiVQkn1xxxRVs3rw5qm0KsUH1+NfJ/CEprixnsSKcD9iI3Cou61LEbb385BcHKvencvznTEfb4Xxc3S2a+mbW2rfkCxsRMbeLC6cDP/fQlHoMq742UJGQ2N/5LH7rebG9HS+LstBa1ioqOo29bL1DJk8aWmr+aYESzwDlUW4vJSWFrKyssDvvWjow9MJhEmlNv/US5L1Vax1LvZqGQekeb1EEQWiEXt9yAjPll9v/HWgyxVUOfzi/yGPHjmXcuHF+y03vZI+tlNNoIbNnei1/6n4kxJ48kj8EeM+7L4oE+iBE/2ORIJ8fIQSarGhSVfWIqqobHH9XAduAjsAFwCuOYq8AUx1/XwC8qaqqSVXVvcAuYJSiKO2BTFVVf1DttouvNqrTovCnaLJam8mpN0Kc8r355ptRb7uyspL//ve/YdVRVRWbzRa8oNACSMTPgrdMGsfnXKcJbaIdYd62EMrEc/3Pv3wTuqhc1uWYz3KRKkAioVOKkY56/yuPsbt6TT+nSGTb3WMqhZ0m+DwWqrJz6SlbeGDgHkcdfzRfMPBoNBKyMtA5Im4m/43Ro0czdOjQwIV8iDJxx5/IsJ3ktT/UexyK0jpqCr1gIsXpld84ppzzumXG3XpUEE5wmuHVu4fOfM3IyCr7Ww0NUDS0l1xkL8IUrUdszAjaKEiq5/ZeB7ikU5HfMrG6LS7XOR/7fJZXoNTSiSprXowkEuJFVLPOKYrSFRgKrAEKVFU9AnZlFOD0mekINDYXKHTs6+j423O/r35mKYqyTlGUdcePH4/mKUSFOXPmsHv3boYMGcLIkSP53e9+x2WXXcbAgQPZt28fAwYMcJV95JFHmDt3LmC30HnkkUe4+OKLOeecc1i3bp1X26tWreLSSy+lrKzMb//r1q1jxowZnHnmmS7rJlVVeeihh7j44ouZPHkyH330EQBr1qzhyiuv5LbbbmPKFHs2LKd10sKFC5k6dSpTp05l/Pjx/P3vfw967jU1NVx99dVMnz6dyZMn88UXXwDw2GOPceDAAaZOncpDDz0EwEsvvcRFF13ElClTePbZZwEoLCzkvPPO47777mP69OkcOXKEuXPncuGFFzJp0iQWLlwYVAYhsUjX56Kqvj474dMurTsX97mHGqvdE3diuxJWTdhIjTWHOpt7FjV/ig4VFau2zufxWFoXqajUph3AEkIfmgTRy/1QdTnPHn3btf3edMjSWykxd/F5J1eU3s3Skn/FXK7XR2/j/Q5vcsW2T1DC9EXrljWUIrPd+mF8Xjlvjtnqs1zXbIVVEzY2WVYn4dzSSH4lwerkNoorFUr7nVOMrgx5QctHYbTqHIzU25KptARPrQxh/F6j8/oJSt+MGn6X7//b7CQpKcnv+0mn2mMb2RQLNiX8zI/+aG7Vdfj9qeQavM+3uO13VGf4dvX0xbntG9JnZzoC7E/rUBy2NIIgREZJTh+K8ga77WuO98+rXMSXjHVt15XqKdsd/eQrzWH5q3EsijRlKPi3PvuZ1KGEIdmRZS2FhsWcye2LWTVhI0nU+yzXKcW3JVg4V+i/xU/y6vEXyAkQuiKSdoX4ErVg4IqipAP/A25VVbUyQHglXwf8JSTw+Sypqvo88DzAiBEjAj5vv/12P1XV2wIVCZuM9L706nW33+MLFixgy5YtbNq0iVWrVnH++eezZcsWunXrxr59+wK2bbFYWLp0KatXr+bpp59m8eLFrmOfffYZL7/8Ms8//3xAs/zjx4+zZMkS9uzZw4033sjEiRNZuXIl27dv580338RkMnHxxRczYoQ97fjmzZtZsWIFnTp1cmvnlltu4ZZbbqGqqorLL7+cyy+/POi1SUpK4qmnniI9PZ2ysjIuvfRSzjjjDP7617+yc+dO3nvvPQC+/fZb9u3bx9KlS1FVlVmzZvHTTz/Rvn179u7dy/z587n33nsBuPXWW8nOzsZqtXL11VezY8cOevfuHVQWIf4k69M4t8fNHDWvAgNoUXlkUPgZLzYOupmc8t+Y0OEkrMAxcy+6a9fwB4d1zcvHFzlKzvO7+u/cazFoqcj/CSqarmcPRzFlTU3CmLEPxeOt6/yQj8srZ0ROFY/v7BwfYwAfnW6ouchr337TMD4ou5t05RXgPUyVOtBCSp3KAX2DC60uxtYjx9ZncdmBzznQayz7w6g3ov1klpZM5qZ205g3YK/bsWHZVa6/+7Vxr+fruXLuy9ab6ZHsO1aVJ2FdlSCFO6T3pn+7M7Gq10WnQcBkKEVvNvDiiO0kaQMPdiN5Tp2Pha+rCfBu6YMUW7qDcn8IjSmUtllPUrV99Tfi+FdR+sE9NWgnqBBIcisaTjnlFGrqjgE/+y1XUvB9dIRqIp7vuB6pvp9zjaKyasJGFtae5dgT3s2Y0bmI63scZi5nue1XNVbq0kJP7Z3nQ1klCELz8fPgvzj++tq1Lx4xG/etdCxYtGv2rl1EayynomBKK4hSa6HjfP9P72RX1mdSw3EMXuXSdO4WWE1ZtH33tC08fTSIXJE3LzQzUVE0KYqix65kekNV1WWO3ccURWmvquoRh1uc03avEOjcqHon4LBjfycf+1s8o0aNolu3biGVPfvsswHo378/hw4dcu1fu3YtW7du5aWXXiI9PXAGpTPPPBONRkPPnj0pLra/HDZs2MD555+PVqslLy+PkSNHsmXLFtLS0hg4cKCXksmJqqrcfvvtXHXVVW6WWP5QVZXHHnuMdevWodFoOHbsmEuGxnz33Xd89913TJs2DYDq6mr2799P+/bt6dChA0OGDHGV/eSTT3j77bexWCwcP36cXbt2iaKphZCkt6/O19pygAryk8yMyK0KXMkHZbl9KcvtS1vtWoqsDdZKlggi59r09pBwJkPzfqqcotoc/1ZYCvik/G9oFLsy+X6H0uPxnZ25Y1w/jM0klwK0pwSTYp9AKoq35n9pyb9IVpcAZZRZ7IamFsX+zrAYtZAGerN7gMqOfla4ooXNapdQG6Xo2tlKDY8NOc4jNc49/ttNM7oPqp4Z/GvQ4O5KOIq3Rimh/RwCYHi780nSpWO0ZYDHExPosvh78jUoVOZuQbFpSArRfbTpuEtTbOnuY68fDHqs+hqK0kNVGvtu9fYeOzmvbYN7gU6x0S7Z98ptIHYtL8Bm1tijUPrB5pChPjkSF4H4Dq8/GbeJWvJYiHeMkEyd7+ffqurZYxwK1Pg87mR0bmRBeFVU6g3lqKHpeQVBiAOxdKUP3nfkb06NRkNamu9EA+EoU4J9y4PRKcXEWUMiUzQFjLPktR1YKtWPI1TA4Y3Tc93HVRgzZgw6nY5DGz8L2K8n+mYbnwhNpcmKJkdmuJeAbaqqPtbo0HLgKmCB49/3G+1foijKY0AH7EG/16qqalUUpUpRlDHYXe+uBJ5sqnyBLI+ai8YvKZ1O5xZ3yGh0nxw4g+dptVosloaBW6dOnSgsLGTv3r0MHBg4YKivAHyBUnampPgPxvzkk0/Srl07LrzwwoB9OlmxYgVlZWX873//Q6/Xc8YZZ2DyEVzPacXkDDxeW1tLamoqhYWFpKY2mLoWFhayaNEili5dSlZWFnPmzPHZnpCouD93mhAn2wOOjKMqqYxqPAMYOvNb2L9cIa2SKSk8ffRdcnkT+C2k/kNBq6joFftv2YrC6NGjXcc6ZfSlsCqwJeXGmmkUW7qTkjQY+NWt3YwOD2JstBoIYEoqRmOLfnBNFfgx+S8c1OfyJ01nn2WKzL1I4RLgZ5/X3KKtReMVs8n3vXmWy0mnFnivaYJHAVVVKKwfQCfDZjyfVcXLf7FRZAYVkjVW1xnHLIOgGk6MuujFi1A1ocWHsKFlW6/LKKhbCVEOp9c7o5a3/xzk9+3nsALoFZuPIbFzyuF+HZxKplprFrW2bG45uZApHUrCltlmjmo0AhfO9100htbhrjQ3jumUrFWpqlUgoDeKewe/1F3J7vpzyEz6D0WmY37q2N97kVCbYqUi8xd0pf4zMQmCIHgyOKuK2hzHmNLPe7Ffv77k5eVjO2ZGozrHXxHZ8vo9Euqbb57+FebyfxH0HT38yhqhJi85OTmi6kkaid/bUojGqOg04ArgDEVRNjn+Ow+7guksRVF2Amc5tlFVdSvwNvaZ1SfATaqqOqNk3wC8iD1A+G7g4yjI1+xkZGRQVeXbaqOgoICioiJKSkowmUx88MEHIbXZoUMHFi5cyJw5c9i5c2fYMo0YMYKPPvoIq9VKaWkp69atC6qw+uqrr/j++++56667Qu6nurqa3Nxc9Ho9P/74I4cP243S0tLSqKlpWNEcO3Ysy5Ytc+1zXhNf7aWkpJCRkUFxcTHffPNNyLII8eeUDs6/wptEjN13Eefu+LPXfqfywhnzKfBKjf2gRskGoMrHKny4pOqysGrsZsMvj9zmiqW0ld6ccsoprnJJ2lBSbTvckkKc+VXm/Ep5G/+uNk2ls7aUT8f/zJg2vi0L/El5PE1PWf46qjJCm+gdoy276eraLrV0itzlqYlsrp3I8rJ57Dad4qVAqyeVp4++67OeAnwy/hf0SugJHpzXr3eG74xvvvC8LFM6FHNJn4btdF1D//4m6kZbuiummb92I8WW0p8jHU5jf9upIdfx+7R7CPWHLkdpG/Rn5P+389npPzOxUbweAJuioTYl3+/ztqT4Sd4q+TfDmhDTosl4nJIVxWUFlQjuApb60BU6AzKrqbG1RcWGTpPss8yL/7Ywf7GFQdneFk+hnK/F4d6pahPnGgmC4E6gxe548cTQXay9KnCZvGx77E9VaVBsZNSqqNhIDsnsvGnnHW5t5/tveE4Vw3OqHfuikELXJY/38S6pRkY1skjtnVFLitbqVisa5BnCtzIW4k+TLZpUVf0W/0/m7/3UeRB40Mf+dUBw/6wEp02bNpx22mkMGDCAlJQUCgoazB31ej333HMPo0ePplu3bvTp0ydAS+50796dhx9+mFtvvZVnn32WLl26hFz3rLPOYtOmTcyYMQONRsPtt99Ofn4+e/b4D7K5ePFiioqKuOSSSwA444wzuOWWWwL2M3nyZK6//nouvPBC+vbtS/fudjeInJwchg4dyuTJkxk3bhx/+9vf2LNnj8uiKTk5mUcffRSNxl332adPH/r27cukSZPo3LmzK1C50DLITHLfDvbBS9ZY3Uxi07RWd0MJxyGjmgn4m1w3/aPmb0x0fs/ZbMzfw4iNj9I5tcGyzoL75Eur17hZ5vkSq+FauL8+07Tuygu9YiNNF4uMlSo5ejPHFfeVoWy9/76qrW3YZTzNtW1VdVQm2W+yKUnTcH9sGaHl1lNP4r/Fc1H0nwE/hHsCTabSag/eUG3Np4PHDaoKENghEuML53PtHPw15ouKmzHbUoDP3farwGkdL3BtX9GrhqReaTjdkPQaFYvN/iz5UzQtKnoZFS03tZsWVEZ/GcmaY/Lu3Ufgi2zQ2EjR2bA0rqtk8szRJ8jhX8BxrzZ2dr6IwoLfoS96kT929bY4NKkZXvtih+Lxr2/u51byKPVbKltvpq5Uj7Ek9qnEa6w57LWOAexXdkC2nuyUQZTYvCcgWVoTDw3byeLqeorTv0VbqiM/yXuikFln/y8cumcPZ3/Fz1jVQJaEiTexFYQTkT/9+DDvZfy1Rf4iDRqbV/jrvBotxe2+RX/QZ5WE4LbeDcJFM7mMr3s4t/9eXibbtf2f4TsayocY3iIUZdg7p25lpLkfV697EDSPAXFcEBJCJmrBwAV3lixZ4veYM8h2Y3bs2MFrr73m2s7JyeHLL78EYMqUKa5Ja79+/fjwww/9tr1gwQK37Q0bNgB2q4m//e1v3HzzzW4T4NGjR7u5/DSu8+qrr/rtxxNnnZycHN566y2fZR599FG37SuvvJIrr7wSaHCdA7v7XaBzctL4egmJidbjG+MvM4WT50fsoEuqyRUI8MNxvwC4thWHBYnzo9QxxX0IcHJ6LR2Sg7kx+VbwhEplVvegZTr0T6UDo4Dv/JZxWmdl6qw8O6zhw3xV16M0joM4t/9eTsur9AqSGy5JGhuPDN7FV+qZ9v6xB138tKQ0cEUXKu+WPuBSzoDC0pKHKE0G90Si8FLRq6Tqg2efU3FE3LaFH3vAqug5lh89xbOW8JR5x81dAcjX72ty39vrnGsyn3sd65DRkL3naezLr3N5PGibDe5W3hYoCgrnty/m2m5HWMiE8AX2wyltKrh9ksojIcRuVjyHlh6WfX2CWH6tHP8z603ZrOCkhp3a3qhoqdWcAfzkVacsoxcAvVNNXNm1wY1rOWfyM33JDS52WPxa+3uOmXsBPzapnWIll3x8D/LfO20Lq76YRHHeIFRWB23LnzIxUA0nK8ru5ri1E06l8MgO11Jia+so5ZTO/m+S411d5sh22KuNhaf7+c7wGA7t0k5meLvzyUpqy8ZjQYzexbxJEOKOTjWQbelMGbEJe6FB5dS8iia14dSHeK/XOC0lG14misY+da7OjP0Lpik9lFva8VnF/zEu7UGgkkLTQLJ0R8jQ+s/CGZEyUPF2R/co4LY1YcIEDhw4ENDQwR+Tt94EgM1wAfBG2PWF5ic2AQUEQRDwVjTlJ/nOCFSdvofKTLuSKRCpGvvks8qaR7nF2+LkhRE7+Efv3ZEJGyY76k7n6aPvUm1tE7ywT+yWRMNyq+mbWctXFTfy9NF3vbK1nZYXWZBcTwZlVTMwq8Y1IXTemt/lBEnv0QjPcy2xNCQ58BxmnNchtjFTtuZdwtb+15JusE90V499lJ+G3xlRWyqgxcZvdWOp1/lOtuCmFlEU3i55nLdKHsPsQ5Hju37k1KYdoCI78CRd1QS3aGlsdXJH74PkGtyVsp5xtrwJfPyfA/cwqWdQMULC+a6oNudQYvKTrMKvBVbg4bLe4ze2gYFYY7Du9lXlzfxad7bf47+9V0BydeTuAM6z39r/Wo4VjAypjhrmVKLxFa7x+P1X2xoCmjfoCRW3f5y09fPuD9y3t6w6x3OepA09bbmmRdpSCELrIZa/wGkdj/NAowyyXVPrWDVhY1htRJBXJiJUW+ihF6//4Qnqjl3Nkfo+WNXwv0/rqi+hyNyLQ+aRPH30XV5RLuSFave4xUOyqzmroIyhBedSacnHUueuFvC8LP1NKXQLIxahe6zOhr89PXLsSWhsPr9Pw3MqOT2/DIA2dfZ4HKoSOCmWkDiIRVML5bnnnuOTTz5x2zdx4kSuv/76mPVZVlbGNddc49q22WxoNBoWL15MTk5OgJrCiUq/PPcBhr8A0XXpoaWvTtHav9Abai5iQ81FAd2BIslyEk6N7XUTAHuMIUUbyiTKI9i0c69q/2j/Wme3WAo2BslPqvcZ50erqFgDjJb8XXtDo6CKR+t7caB+KCSFE2jRd7u5BrNnIjQ3Xp+k0j3jIF9FqEcz6uz2JzqNnvPbF2PVJVOV4TF48ZBNp3ieV8NxDSqfVdwG2Vv89Oh9nrVpB3iQW7iZlzGjoz3H/cobaiB8X9Rk7AtaxmZwD2jUM72WpQP38Kyv+Ms+ROmYYmJQhpHIkne5P3caVFdcIX+oGi2oDVZkCqpbnAcnr5QsAuDSgj+ieN0/jzYda2dqU3IrNxcqWI1a2u2vovyk4MWdZOosjG9rd1f1/E0HUrCF6sLgi5q0/ejrs0i2+J9gKMCS4wvRWju7tt36D7tX1WuhonG7CnBWQWlIvytRMwnRZmxeOdsrUymu9071LjQvuUkWezzFdvYEKkN9uKcHokdarV93eN+7AwcQ90VHpRitks/O99phswL5DceczZRZOnLANAQ46hq/mivOYBln0CH9M0Zm/wcca0NvjtnK5Wv6BRzzebZvNlRgNrhbft3Xfx9H6vuwrHQkn1ekMzH5/oBtnVJnD1vxxGUqzvBW0fjavjhiB3P5Fq3FewHh0cHNs3gsxAZRNLVQrr/++pgqlXyRk5PDe++959pu7O4mCE4ax4q5YgC82mjuHWsTyp8YRCeOuE34w0or78BXDeequDG5iKL8/LA+rgaDAb1WcTMcVxwqJeeAwqKtpT6pFHznEXCxcMhO2qfUM7fRvuE5lTw6eDc3rO/FtirfEZQ1QClZAdv+X+m/MOsrgU3e9etBMahYtLWU5a+jbZESUCsW7KpP6gm/1jrLRj5UOVlfzA29DrrcKwNxTdejlPs8ogSdsPqS0Jhi1+I8xdVAaC5twUhOTg7b6sTX9ZvRucgrNoOrVR8n88boX6mpz+JhPz241fdu0Q2dRqXe5ueeOqqois5D0QQPDdrtdR9tigUUG08pVwNu43MvLEmZaAGrzvd3SUXFqq1DH+Rxi+SdEQqfld+KipVG4Sy8CdL1/UO78G3VnzhaPwcLR5nL9WQkF5FsbOsqY1EN9oYanWek56RRoDZjPwCZxwa7HVMaPakKUGbt7HasMeHmCLr15EIu6FDMfR6hPgdn21+QfTNruLLdfl401lPhz6pQNExCTFB5YMBejtQZmLmmf7yFETxjZIb5rntp5A7+Zmxv34j6GoW9wTTFxBenb2Lbm64MOV6vpw/K/kGltR1tDA96vT83cjL3dO7IhQ7DrV+Th5OmrabSEso0PvD18Df+GphV7WVh72RQw+cmqjGgrDrvRVRTpZby3Wm0HRId636heRHXOUEQokaeoZ5pHf1bdQSLohx81T1w/Q/5Pf/hDxHVDcYfTrLPgKuyt/PV788Iq+6pp55KdVu7m0m9Dk477TRMinvK8vI2G6nJ3IMtyDVon+LtajMq1z75GhQgW5YmNYeF/JGSLO9YRC8wg58Y5LeuMfkYWFRAxZxUDkBNqruLTFPm5uFlRXHH20qpAc8BVHuPGGFh9dtE5UMoH9uCggLGjBmDzRUQP1ifvs3SwT0jXQPhZ5T5pOwOSix2fzinoZCq8T2xtzXpEvmWrTTvJ0rahhrjyD3AtgoYU45iQ+XNKSpVmRWU5v9EvSElYCvB7lWW3syFHYtc2waDgby8vAA17PxmPJ3fTKcHLKM62veNQpHZfi8qrO2o1tqDl9elFTqO2vnPsf/y4rGGGIa3pZzOyvRAKrrQcDMUUwIcw/u35Xy3HzAN4aOy4C6uUzsW+3z6kx1Wrc5/I5kYfnn6Ri7qVBS8oCD4wPnI+foWxxr/74bEoF2aH9/pGCp9PRVLv3O4WanA3q5dSdWpzOzsy7S3AUVNYv2Q/8No8B0vUrUvUzBkyBC0SU1fYE9LS+PktvZ22irlANTa7AuBOiX4yORrxpCZE1lUQROebvb2J9rm0euTQ3fSKUg4C882wimRawjtWT74dRtKd6Rjrm4Yd7QAm2XBgSiaBEGIGv8cuIebex7y2u/8fGmCfD2bYtkSKlpHSrkwMtMD8Pu2ZVHp36KzZ58s1tk/9lmOLG+qQyDPa2BVtayumNVQ34dexZklL9DV0ySluxVSsMdcKre04xDt+dB3klDM+kqqsndwPL/GXlV1VzA1kiJA7/5o+ujTGuAzlqsz8vCgXa5tT5GrNRqs2jo/z13obkmhEIpFSWZmpkefvvGlkPXc0yU1gN9iGKey23Qqm+tmAtAh2T6xMmZ28dmn63ceSgcelZNSknmfs7ysuVQ/bqn1tmRKLZ0d/XoovhzX+ogmi6qs36jIrGdidzAm2x0DrbrA7i7B7tVtvQ7yl5Mb3nODBw9mwIABWDXhxK7wvT/XYOH90/y5bzY8hyqaRk++p7warDSk/Oy+91YKS+9y1G8K/q+Lr9/H00ffxWhzt6JcUXYve01jQuzN/3Me9DwU0FtTGL/7ElTV/X5rFHx+pwQhFOI1yR2eU8n7p21hTG7TAl/HCq2iZ1zny7BqjFh0Nc3Wr7+37pH2nVk7ZjTnDOnMdT0OB2wj2dyHiuyeHCmY6vN4Re5mitt9R3Z2NhrH96MpcZ1GjhxJas/TsKgNSh8VG/UG5zgzBMVNyC7i7lfon9xMSSOz2gNau1XUfr0upH6daK0qGbWRj4v6Zfp/RtyeIbFMbdGIokkQhKjhuULhikPkcKPR1tu/GFZVR6Ul+Ar7HuMonj76LiV5a6nI/tXvZL+0zToqcjZ77R/U4xR0OTe77Usy29vQWgJ/UNN1FrfzOSnN38pOZKMNf99Oz/0HTMPYUneua1vn463d2H3FH77GJK8cf5E3ip8NKIhTAWbV2vxY9fg+k2BjoJ+qL+arypsDtBAagerObLOdkbkNvoie7nE/pGdTmv8T1dZ8twFf6D2FLnk0Jye/1J7vo31PVyWFX2vdlYeBLAatqo6fama4lS23tMOqMaIq7tYj3jGQ3LcD3Xt/g44u3U9iIwOwhjhB+bT89kZxzdwVCQrwY9XlrKuZDoDVQ8Md7NkMNjBK87AWS05OBqA8q2tYmRBrU9p6uZRl6wNnzXQpmtSG57nB/s3/iWXXhZ/Z8Q9djrL7ugBx3wI9/x6/tWpDOnP5Pyxad9eI9UP+j3VDb8NoS7PHWWmE2oQhqgL0K5lIv6LTsJnHRdyOIHgSrxBwztiMgSyX40maw4urtO1ayvLWux1TfQVcixKe98P5Hvy1z2X2HfrAFqz2RgIf8OXSFQqp2sArml9XNiwiVmTuoyJ3M4bkZK83a7LFd0gEsI+7A/XjSyFVpLahwpFQx+jwfasP8Lo1oacKdxn+9LHKS09YQw5uDpChC5YV2k7jZ8isTaVeL8G/WyqiaBIEIWqk6ZNZXPSS9wHHV9P5wllVcQOvFT9PvS05YHtbaicCYNMZqU8udg0GzLoqzPqGVT2rvpb6JE+LI4XczkmU520MWf7GE6dXR21j2an+LQuausxSrdVyvO23lGiS2UG3Rq26DwpCsfJyShIoNoFXKxGNlhvaD6xq8LWtMrl9Q1rdb8znYNXaLUzKk0IbfPjC8yM2KreSk3SlbKs9g3pbaBZCm2vP46OK2wL24z6pDv/a2cjlP0ffpNRiz6CmVdSglj+q4jv+Qrmlg9c+XxI5FXmhsNt4Cr+aznFtr6+5kDeKn6W07Voqs3/1W8+mWFxWSE63z1CujuKpnHL8G+qv6pi5l1ttTxfK9TUXBW3DhuLDjSDywO2bhtzK1v7XhlRWReHH0fdSmTbUbX9ynYZXil7wV8mlwLFbNHnL2StnuM+qrqutQlmbDdSmBrfoufwkT3eT0N8vntulyfaMdWZDuWufXrFRkd2TyqzuVFq9s4gGevdZFJjPjVQGmMAqzZVKSjihaKp1a6Q4Az/HUGfTJJzhBXxhTYnddNNz3OPcsjiVE03QDAasqgZ/Dq7uGvg9e9zcMPaz6OzjIZ1O7/XmyzL6X5hdduoWXh3l/o3+ilPYkub/mv9WN4HXi591LPj6t4118h8u51Fm2V0IHYuPp2xzfItCVDQpwE2NLEkbLwBZVN9WxjrFxpdDH+Xb0/7lcUTMnFoKomiKEaeeemq8RYiYOXPmeGW0E4RQOFzfj1pbg9+4yeZ7FWZ/vX3V36Im+TwejPK8jZS3+RmbquHDsv8XURuBUFG8Ur9Hm0JDEmhsfJPSkf8y1W85JUgYXa2ickobe5DEP3U/4nU8N1nljt4H0HtYdaia8HNBKH43gk80zyoo5bbeB13blTlbKc1bB0C1IUw/xkZkat3zpD00aDcZipYvK//Cltq/uB0LFLRyv3GE27bVw0rGZlYIetIOOnToQPv27V3b53RT6ZPbGwtJ/Fprt8L54vRNPDt8h3+BsAe39kUoysdw4yV5/hYP1/drcGPzUrw4V3m1lBR8T1l2vUMuO4EUNcHGpKrG7KZE9pJTV41Va/T4Xag8dXoV5+bs81lHwTknaLhuqqqwXD2Tf+KtjAsW3DTcDG4KCuM6zXBI6n5tjIb2mJJKXPs7qXqqbb7jPakojSa5nqo6+F2PTpzcbYgfGRqGexZ9NTWZwTP5BJ9HNRQwqm38HnMI4PbH6NxKPjv950aHvTvz2b1jZ63eRj1JVHqYeDa+Jp6WZwFaFYSQiZeex6nID8k1OQ44swI3N/4WLYI52oagJwrrWmsVlT4ZDRa5KSkpGHUNrsMVlnZUZHb1W78pdzXPY7FuNWMod2S9UBXvcXixxe7+XmvLbtS/fwlKyQGgMvtXigu+cz8Y4g9CRUXfaGxwvJHu7D/H3vJZ5/PTfwbF/o6vUkRl0RKRuxYjvv/++3iLIAjNRrrOgl6xeSlFTKq7uav3AMAdzwm05+TDc7vK2pZ9ppF+pPL90XTtDXO0GMqgJDr4GzT55uIh2bySdoPfCfw9p8H57UsC+sOHTgD3sSDXJ01nY23Vpe47lYZJc6R005d67bM5YvaYVXdFTaBBo01RqU7fi1Vrd5E8pPTHqgk1EKY7vXr1onfv3q7tty7wbW3WO6POa19jwrEf8Qq+7LO96E2R2iWbKMiwX5/q9NAD1PobdOgcolXkbqa8zc+oKqypmulVrixvA6X5a1EaWTBZ9DW8yQVs1RVwvN3X1Bm8g/QuL7sXi67BheKZY8vYpAwIWe5Q8Byo2xQLVo2JLEM27dJ7uRdWFI63+5qj7VQqc7ZiSrZbDx1JtZ9D4F5UNusLqNW4TyCUdh1CUiA1xtTVRM0I3+8GFfdnxupn1RmgxNo3SE/uz97UDnbrxorsLZTkrcWXCjKU2Gn+yNJbIjZk6JNRw9i88sgqC62eeLnOJaZ6qQFNgGWELt6f6ajh/37YD5Qb2jOX/3PtNWhsXNznHr6qvNG1L8niJ4h5wI7dN6/peoTnhv/m2h49ejSrMhssa18ue5R1w27naFvfVqdO1CgPNn+ousprX5Wh0dimUXdHLf0CtlWfXAJACVnYQlb8NB75+64T+JtnZ68h/MVRIf6IoilGpKfbJ9hHjhxh/PjxDBkyhAEDBvDNN9/4rTN37lwuvPBCJk2axMKFC137zz//fBYuXMj06dOZPHkye/bsAaC8vJybbrqJKVOmcOmll7Jjh311/Mknn+TOO+/kj3/8I2eccQYrV67k4YcfZvLkydx0002YzfZJwdNPP81FF13E5MmTufvuu71ebj/88AM339yw2vvdd9/xl7+4WwgIAsAHYzfz0KDdPoIDO/Y4JtmKy+1DRcXm9aEurB/kFacjMP4/yEEn1WF/y0NTjjUVX9OtQBzLHOwo5ft17lrTC80Yx0eBxu5y/l3nvKu7y21VFbcYQJ5oFT1J2jTS9DnBJMOEntLMelRUMjyUQVbV/2Ak0ATBqquhLv0gVr190l2VvZ3StmsaCoTwvNTZMgIcDd5AqKunoZTyPUUP6AcQUt/ljrgOTw/9jVydu0Lnt7rTefrou+gV/5aKNSltOdDJO/C8RnGPo2FRk1hXc4nfdnxZwOxROgJQnVzvWZid2rYEt6eyEyw9tudRfxODsrx1lLZdwxNDtvttq8qhC7Vq7TKXpHhaBnlj0VWzTd+WrwxnA+GpaT3LVv+uGuNA34HjPZ8Xg+KtGFVRqUs5jIq79ZDns+zcsmqNmJKK0SlQbc2lPrkUm87o837aPIO80xAfzPuc3feMyo08FfZzw3/jgQF7I64vtG4S1HMt/gT4wKabYuk6576tev3hvnlZF7tSf5sjxh9Alsl3MpRA30zPI84YWr4oJYuy/HXUpO/j135/dJPJ4mPsprr9bfVpbRTKc1jWZgPGZO8Mm+XJDe9y59gxq7o3n9Td5do/33Yr/ynxHcPzSf7Iyok3YNH6zkDrZGxeg3Wy5zs+qUkWcPIrbCm0evXg3TsL2VIdeNU4XAakp3D/yZ1CKrtkyRLOOecc7rrrLqxWK7W1/l9Et956K9nZ2VitVq6++mp27NjhWhXPyclh2bJlLFmyhEWLFvHAAw/w5JNP0rdvX55++ml+/PFH7rzzTt577z0ADh48yCuvvMLu3buZMWMGCxcu5I477uCGG25g9erVnHnmmVx++eXcdNNNAPztb3/jq6++4owzGtK2jxkzhnnz5lFaWkpubi7Lli1j2rRpEV41obUzNKeafSb3D8e3B6Fx/EDnp6E0vZCa1FKUGvcPj9OtKFSiHSshkCLCe8Bho95QyoqyuxmW/xA+5kSRSuGxmdjrmC6bpCDf/XFtKlkbwPBl+sl/BI09YPGGox+xu3yd37IfM4FDbU1klVZAnbd6s/FfH5fdCTn2SbxZScasq0Jv8aUQCnwCqpuSwndEoe8qr+HM7IX4oiFbmHc/9sx3Km08FDftDIG/XY3b1NNgOr88uYAza44HrOvJHm0OtWkHPNr2cPOyZfBG8bNoDcvJzvC22v251h58O0WfSZnZM2aanfXDbsOiTydjz7+YedJxnOu/Cp6D68D4+u2b1BTA2zqnNtkKyQ3ZB70682rbfq4GpQaN4l/1+6XlLEwBhlE2h/IoNynKadAd7wSr66UT2qC7c4qRyR3K8R9xy500rZVKc4PiV68YoZGVoIJKfVIp1Vm7sOpqSa8KbhVQl36QOkBXpvLK8ZfAsZId3HVO5YHTzHyuP5mc47Vep+xZOy/JTLErD4BMSoToEWkMt9aONuDCX+zwPw50/93b0KDFhkFj8/GVcOBoKklj44/dDhNI3ezZq1ZRqbVm8VH53zk5+Vu3sa8zkLbZ4O4Wvif7GA8wG1jl2pestaHXOJKwaEyUtl1DWmV3v3Icow0p+F4ssOirqcreTvLRtl7HKrK3Yg5gb1KvUShL8a+wr0sv5n8XX8T5tk8Az3h+dub0OcAjbk003JMLOhznex9im1WDq9g+OqIqVhQ1agNsoZkRi6YYM3LkSBYvXszcuXPZvHkzGRn+V7w/+eQTpk+fzrRp09i1axe7djUMjM8+275y2b9/fw4dsgdT27BhAxdccAFgVwqVl5dTVWUfWY0fPx69Xk+vXr2wWq2MG2fPutKzZ09X/TVr1nDJJZcwefJkfvzxR7f+wJ6p4IILLmD58uVUVlayadMmxo8fH6UrI7Qe/Fu51DuyzTljmtgULZ8xlprUUo+a4fcViHqbe9aO4+2+pi65ym/5SChJslCRuwVTchHHzH18lqmytuFIfW+fx/zhOa0NNkVyKi6CWVaFO9VSgphve7XnUd4z08nxnBF+zaMVVKoza7EpdmVJryzvQVFjaki1d6k0WFHUpO3neLuvUT1k29Molfr3WRf4DA5/vN3X1KYHs2BouDOKpr2fEqGs3XjfidL8nyjLX+cV+ydJ4y92lXs5FYVUpcGy6+C+51iR0jl4vUa3bF1SZ2oy9nmUd7+nZtUevF+1tcOiJlGkaXi2R+RUuiwWA1qOae1t/LnrfioMj6I67rnNZzwd//ieXIT4RglSzKKm8FLRq3xXdXXAcl/rBrBG1/i377vhdzUTHO5hTcek4Pqd2JTwBt9PDN3J9I7uCsiTSgdw+u5LfZbfVHsBr5c2BCYvT67AbGj8Hm0IDGvTeGQc9XoG3HcUeFqd+VQ0NdTJNVg4rO8K2CdP/lBVhXN+uwJqeqHRaChu664QFZWT0FJJ9Gc30IRSpwS3VI6Udknu754amz3+kOf12mEaxja1E7ogisKe6bXc1Xcfl3Y+HvCi12RomDBhgmusYyOVxcdf5pi5N99WhZYUot6Vha1hYefvffbRKcVMXcph6pPsY2VTsq+FI4W2SfU8y5U8xiwfx4P0nVxCoRJ4rKWGoFTdpXgrwcotHVheeg+lajY2baM5QqPr6SuDMsDiosWuv1/mElcyEiOp2BTnd0OUvS2FVm/RFKrlUawYP348X3/9NR9++CFXXHEFd9xxB1deeaVXucLCQhYtWsTSpUvJyspizpw5mEwNEweDwR4bQaPRYLHYX0y+zPWdLzy9Xu8qr9frXfsVRcFisWAymZg3bx7vvPMO7du358knn3Trz8m0adO44YYbSEpKYuLEieh0rf6REcKgW1odi0c2uIV4xmjSesw2DmT0pYKGCIBNVTT5m9jb83m5912dXsHvx45GrXFMaMMMLOjZnkljP1er1ug3ftOrx1+0/xGC/7mrXavO483s7YLiJomi+CjljafiJ9RBq8nhkw/O7CJOd0jf8vljrWaQ32NtjF2oyt6BqrGQUXly0BS4jWU/rs/jX1xPXcZ+AOpI8XtuRq3/FLmWpAq/x+ydBj9PfyUK1QJ2qL0ClgHvd7pWUalNLfQljNe2CQPF+T+QXToYnTWVKpt7lhqzLckeANShmyi1dOK/xU8GfjY9ujHbkhwh0rV8U3kdhzVDgE0APDJ4N/91JBXM1FsJltPMoLHZA677uVnBFKc2rH7vidfVUaGxDi/YnaxX7QtCu42nMi5zERpFS1ZSW8qM3sH2IXhMjT1KZ9AZHe7C/twFQvsdrclMAtyzYdo0ZupSfMvWGLOay6rKGZDdsO/cHX8GYAufe5Xfaxrm5rJRlFEClHiV80WwmGFV1lSXwswfjZ8Bd3dG/y4XJnMX0vW1mEtmosk46FJkCkK0kBV63yiK4vc11kl7DTAvJv3mGMzQSG9da+sIbPMq90X5nWhUHQOTr+Z4gHWNF0fYw5BsowdGAmdGBvt5q6qK6lgA8+R/TKTU9dJVPayj7Vx8+kCOO1yTnc7B1Vm7vMo1RqcxMK3jcRqnFLGpWo7Vn4xHLhOMyb4tjkDFaAmsbAqGiuL+gQW+rbqGg/VDWahcA2mNFxWCjzzNqvt1NOvsCwufG66Egh/JPyoGDy0JeV/GmP3799O2bVv+/Oc/c+2117Jhwwaf5aqrq0lJSSEjI4Pi4uKAsZycjBgxghUrVgB266ScnBxXbKhgOJVKOTk51NTUsHLlSp/lCgoKaNu2Lc8++6y4zQleeAaYriQZi7bBPVTr+YbxUHbs12VQ48OSxKKrxqrxbQrcGH8WBxtqpmG05rrtM+vrsepSKMuy/0bCVXKpHr5xrjNR1ICT4kCZPHxRaXHv51dtRzdlwz6CK8/7ZNQwIqeSAZnVjPVT3KL3ZfHSCMcpGVOPuDZVS4PPkeJRrineBA2rZvYBWDDXhIZJrMqPaWOpoyG+z6PKn9ms9KAyaxv1ev+uyhFK6pIAGlyjnNTZsqi3peDJi8pl/JruXtd36+7HtIpKTeYev1I0rreT7qhaM3WphwEw2tpQndEwUF1Z8Ve+qLjVtV1kPtmvHA3tuvdUa3OuSitu6ehVReW/TMHouB6eVjONMSWVUZX5W1BF0k5OoirzN7/H97fd4X08xIcwWN+F9fYMhDbHEGlowbmc2fXPpOqz7PX9emoE7r+44DtK2v4YkozhYNOaqM7aGbTc99U3sMP4O9f2P/ruc/19ahtvResvueVU5Pzit71gzqbuW+6LAj9oL6CkwN3ayKZx/z3tb/Su0yoqv2JX1tam+VK+QlZSW0xH7QF+Lbpo//YFwY4SJ9e5RLfh8Jet05diJbr9eljqKrCl9myqM7y/nRD6IttbTAmpnHMRz5+l1Gb6cgi7FbTFUEVxu2+9yhxXPDONBg+hoFftLsJOBg4cyEbLLN4pfcirbFW2/wy3u0v/TEX2r5j1PtzkVNj2Zge/dV2Y3ZVVoWRmtfoYK/kk0U35hICIeUqMWbVqFQ8//DB6vZ709HReffVVn+X69OlD3759mTRpEp07d2bYsGFB27755pv5f//v/zFlyhRSUlJYsGBByHJlZmZy8cUXM2XKFDp27MiAAf6z70yePJmysjJ69owgK4NwQvFO0imQ3xBbR+ewGrJp9T7L/5KUD0kHvfaX5dkVspnVSdTTEOvF83tz3Ozbb31DzUXo645DW+9VrYa23D/cPbJH0iarN3Cbz/IVZvfXZZ3bqCoKX0KnwsZj0LRGdzI0UjZY0FGJd7raxpPnxplP/GHTF0DoycKwKXpCGerWpRylOus3jMbQPi/H232N3pTtti9YMGY3OXyU3aL0xJRynBJDGW2O+9a0lZi7gO/HMoR+fd/vTbnlbDPdAUmB3DQVUlNTKScDbZABuC4jA1/2GF9nJXEGDc+w0ZaJRut+HYpt/SDtMMl17dBZ0vkpx4jB2FgZoYakzPUQnZr0vaSYrBi19VSn2p9LU5KNHfQgOb2YDFNPl1w6xcbwnCrWlGa5mqhoY/9NKkFiNb+jOQ9SjwYsYw2qSIzsd2lwuCxa1GTqbcnkJLd37E+hlgpvtzDVGSsryHMb4LlWNRaq031PjBrqh7Y2aEoqxt+5l+Q1BLg/s6CMHY5L3DWtju9LsrzKmwNY+qnYsGrrXFtuonqVdQ+MVWRwf4ftqkqmtM36hnNAz1LlfNe2VlFRsKGicWQu8la3dkxv7MbY8P6p0Z4Mjmhg/p6I4TmV7NQn+lReSASaY947MqeSXyrSMdkafvOJPt/W+JGwOnMXGZW9fB6LBp4uWHuUNmyvnIHFy1rX/vvekzaU46lfk3fUHlIkUAKRUBjVfhqVxmL0mv81qZ3G+HINP2XfVLft7PQMjiU3LBi1adOGzcY8ssLQsSvYFyrqk4upTy72eTwkajznkP5qNuyvNxeE2roPVBQUeuSMYHfZ+pgrM4XIEUVTjKiutpv6XXXVVVx11VUh1fGnKPrwww9JTbWbEg4cOJDXXnsNgOzsbJ555hmv8p6Z4RpbUV1//fWutm699VZuvfXWoHKsX7+eiy++OKRzEITGaDwGAP4yoznx/LjuSzcBm/2WVxvFCGmMWV8RNDW983N3fvti7uh9kKeP3tPQrgo2VcNe0yicltM/lWa71S9PauwrHuhzHN7kJdiH/WidgTdSvP3x3dKQo2BBR1JjTZL/mNnuux3X06u4ogVUl+VEg4WCu4WT0WFRU60kh2zNZU4qd9sOtmJca80NGHzdpuqwW3P7H3y8WfJEWC6NoLpO0qap9+uqZAqoZIJ6FEaNGsW/GQVAp04NVkdJGnd5U7PT8KeP+ZUGxf/SkkfolLsEDGBMO0x6VYMCtixvA1klg+19J7vnmC5tGyxukEeMJkMFZkMFFr2WkiQfGcgc18epg/1z98Nc2vk4f9l4Mpsr3K1tTUoeNakHiC4N8lo1Ripz7LEdvAKNB9MHOUqb1RReKPovqmp/phcM3EOZ6Qh6j+czWYsj51rkioq6tMDOhhZtLTqrb9cMT5zn7YkK2HS+34vJmvAH6hVZ+6hLsVuvmVKKqTUXklJrD96q9ZxEBAnerTlkQM1teF8dNfd0UwTrFJVT2MD3jMBgzAO8Fyj8OevVpgY+t4Kkeh4dvJvxqfB4MJ9P4YQn1gqfk1LreHjwbj45msuC7SfFuLfo4e+7HWvrwhqL+7jSpNH7uUd2+famDnXb+1bJo03qv2NGbzql96NL6hsQhVNVUFFV93OyaerpVd7HzXI1pXNnduFpmR6+rX5TTeXKyYLqfvaxo6rhoGkQRtVXPGLVbSE19GQ+qpebtU6jMqN7HyyGc1HQsrMs+pbCQnQQ1zkhINOnT+e3335jypTQTEgFoTGKEp4u+4ifoNoNuH+YjmXvo7jgO69S5W1+pjQnsHWA02rmkk7eqV8BNtVcwCfldzYq77udekMFJj+mMWW5m6hLDW/m4s/83IlRDZ7+/F0m8k9utpfHwH9sV1KKh/WXomJKKvFSBlXkOhR7PuZmjUWrTLF5HHO049A2bK4fQXnupqCy+urBmRrdM0W6k2Muty/fx49buvmQuGnUG2qxau0WQKaUopBclXxh85ApkKWov/MHeJvJmHQ1LoVqubXBvN3Tpc+mdVcKBXMdA9hjMFHayDrRrX6Q6oqj/c4pdtkyHIG+z+h8jqvMgbTfU+sVfLxpNL5a9Y1ji3kW1Pek3IdLWF3KYUrzfvJWgihW6g2lZBtsnNKm0r9rZwxdasry12FKCi0+kj88b1sJ2Vi0dvfnYFkjvbG5lExOajL3UJn9q90lztbGTV5f7p5uspW5a47f1Z7utq1TVHbYnFYRKqbGgaZ89uCfUksnTLYGpV2K1v589s4N/JsTBIi961y6433ZOSVMi9M40zUtytk1Q+TncnelhpV83wW93nH2+1hm6YJFV0N5zi/YIri34YZHCMY7FfeQvv0/bvts2voQxxyRyBLo5R/8w1BjzYPafhQXfEdl9laWl93n2zVf0RKK2qE892e3bVVj8XKzrss8Tpu29kBUBm3wOFpC/BCLpjgwevRor8Db8+bNo3fv8LJTNQfLli2LtwhCC8aYnBkobqsXtbbc4IUat2+I3UqZZzDlw7oBHG/jbQFjMVTyG759zS2GSiyGID5CHtRl9aE617+ljeJ3ra5h/xYaFHbfms/giL4NniLWpR7Gqqsjo9yPcs+mhDiAspexOUyMbFr7u+1ghpmwfPMaoaDSKcXIq6O3Me/4efywqxTVXMdNPQ7x4t72OAc/xpQiLDpfGagCy62iYg1zlbW0beDAnNFAr2tPY/s8xaYGtNw6krsPxXqInNIhHj8zj/P38chY1cB+g2Xpod+7hmDbTus2x7+KuzTZGUMoxe66VRtieIbwaOhfseka7XW3aapPS8ecVOpZ2RV8tcjs7taV2raIitQajplzaIff+UrMqczZ2sQW3AV9kmsgfz35R8eHr5L1p3h3KJeMyWbqUrc26jLYb9K9wXKN+wOSnteBEk2mo28btdZ83AKTqx7/utuwubX13+InydEWAou8jn45YVNAOQUh1hZNzvb9LQgkqio0Rdu0RAeR4qkkV5Ukn+XM+kpXFjeAitxfWMjVaIGqrB1Y9NUcL7B/F02eZrAh8IlmDMVtvyOv6LTwKnpQm74fgym8sXCkGBUDqsb/t96mMbN80m2A77h4ANZGC62eVtON0SYNZ31qQ9m11nN9ljMbgiRmAcpSKygjx59KUUggRNEUB9asWeO1b8cO/4HaBCHRqUfn0wzWa5U8yLK5TbGgCeAvH7qpbXBsQUaLW2rdP4J72/SPWt+BqM4NbNXlbzV1W+3v6JfsnUTAaMv0Wd5pCWML6GKouv1p1vtPNqBX7IlffLkyhopNY+F4229pW6pycnodhymA/N4MSK6i/eFPuaBjMSrgdAxqbLXihusa+b7JdWmF1GR4B6FvDtQAildF4+EaZSOgoglA1Zopzf+J0kYrhU7LK7/9oLCq8kZIDcd1MDgNV933da/KavjO1XplCogCjm6taiYGN9cDz99M4B9/rUaPVWvEoqsiyZSPRWcfiBsdqXw8X2NWrc7RS6JOAe0ES+sdFZzKRY37tic2j4toTArsFqjXN6RR8pVyO8VsIa+3gTrsQacapwNvXNrZbZnVO3ab85gFrZfloSA48bQ61ioqS0/ZwjO7OvJ5UfQUBL5+rUZbWkgWqc1Fsjad/NSTOFi1lcPqyVRlNv9cxvtL4vvb4ulSbDZUUupQVjhjzVVlmTlCPi+oM0Pu32woR2tN5ldNDyDy8Y8Ti76ayuzIFhXMhiqOFwRPJuVklW4I5PlOUgUBxliNsChqSH0qShJV+uZRoAmJQ6tVNKmq6hVUVzhxUFU1aMppoek43cnm8xcyfUSTSTNoKK4D55DJ6sfyx4lVV4PG7B2Q1kk072iZLxdyB+aUXOpshxN36dAH31ZdS6FxJPCha9/m2onsNJ8KSfv9VwxoNR34AtQbyjA5AkjqAsRDChVnMMqKDBX1WENMr4yMDOrzBwEH0ShqUA8l54Rf1Vio8DFg85ldpZnYWzcKsrf4POZ5K+ptBp/lguFyf3RQk+YeC0lFpTIrehMCxRHmwZmlzakMdZ6PzeVVGeuAnQ0PhtuqaJgmSL/Rlep8e/yqnOKUhvNwnJ9JSWIvOQ0VHEG6VY2Feq3/Fd14U673FVreTnMPlzwny9u7dvNT0k6yvrHG1ceiBipag4LF+R3yOJ+Ts/txUmZXvjr4oXddrZ7ddAHsv5PnuJxi2gCrgpyFIECq1kquwcLskwujomjy+1tU0nmp6ClSNSuAjU3uJxqc3uVyMpMKOPLbTlYknYJNOdbsMqhKhptls0UD6ALHSgzETwx2WWiHgqcCy5h8nGRj02xtAlkGBSKQdVKssPi1ZIsNxW09w2XIXD+RaZWKpuTkZEpKSmjTpo0om05AVFXFZDKxd298LBZOJBrHs6jE23JGU52C1dA4PkyQhNiKGjimUTMofrbXD6VOp6U6OfauUpHg7wratPXU2NzjN31deR1WP0GrcQX/9X1RFaC2kYLCbknuJ55TI8miYdXRYLXeSHGQ2ZlPGU+9Zi+mFO/sKO40cpPysSIXyipdrKjXemcM9IdJyQICB7UPBZvO3cJJ1VgxpTRhQuCh6Wtw9dC4bSuozOX/0HXcC6X7m3E4qFKXdtjv0WCD+OqkhutVlreBjHqDo1U7X6VeSg3ecSHKc35p9LtKHKozdqOxJnM8I5Clm8KECRM4cOAAe/YEyX4XBTzfEocKAmcg0hV4hhbweAYDLSwpKgM6novOmoZeWeF1uKDnUF6jDRPVFcAuh5JJEHzjZajt+DfmwxPFPsaq1wwlURRNOUkZWLG7zfmzErfoamJq7VmnOc2VqRigOr0O0kO/PjbFgqppsETSNtEqqSp7G9Yq72QZQnRofK9A1EyJTqtUNHXq1InCwkKOHz8evHCCcPSo/zTO9fX1GAyRrWyfiG2pqsqRI0f417/+RdeuXaPSpuAbv0FxHazteDLwEwajfeAeylCjOnO332ObTBNRopzG9JimHa8ziXRtLWZDBW8aJpBi1oHPxPLxp8Ti27WuLG89bYvdM9SEMrhrXKbx37UpNhpndrJpLJTl+Q4Oba+sRO2Lb1OA7C4ojpTkAKouhR8YTnpOl+ANNIeLUATYlHoC/Qo8L58tBt5lscSqq6M6Yxcak/1MnOs8+jbdSNNXYmuCW2Uo2GK0muu8L2Y1hRprNjU6P8FHE1DJBMEz2h1v+y26ko4AdOnSpXkUTU1aBFS9JxsBfvM2rYmy/PXkHB/hM9mCIdU+gV9nnUdW7Qvg8OJLS0sjKSmJ0tLEtVJrSYwaNYojR45w8KB3xsCWhOe4xzMWXTSosBRgU+tI1mVwUuZAdpR+j06xYQEy9bF9j4aDRgGrCtpAZ6+xUZMRu3dKva5pc4e6NPfnUROFMWa0E120JKozdqO1pFCd6X7PY/V9jrXLeseOHTn99NP5/vvvMZub32KspdMqFU16vZ5u3QKbYScaN9xwg99jmzZtYsiQIVHp50Roy0l5eXlU2xO8CZYhzRM1aMCZwIdL2v5IUl3b8DoNwmb9MMwYKGuUYasugItJvCk0jwV8u15FoulpHByzPkBWq3of6ew9MSYfi8pku96goHQcTgXe1lj1dCBYkPFEjZVTUvAjqdWe6Yj94y8VfZNpogtboBghdWmHweSwR2sUb61vRg1WfU2T+g2KawIY3ftvD5xez/qai9hiuh7aRTe2VdzR2CjJGuza7JjRl0NV22LapbUJiiZf1mqhNGdKPk6fXO9nQ6u3B6itTi/ky8pbXLHLRo4cCcCqVasillVoIDU1lR49erR4RZMnw3PsblpZTVQA5aV0waBNISsFXi++lHbarzilQ0/yUjtzuPo39JoSx/JX4thwqMFSkDoIpuxuCoX5gRNbBMMz5ls0Y4GeiPi717XpB3zubyr+MkJHixEjRqAoChkZGbLoEAEtbL1UEIREQhui5UjDSkbgL0JFG++U456YkotC6jNULKrvDCWJSmW2/wmg56DPlHwciy7w5N5iaIhl4BlrIBx2tU+iKju6gUCtPj9RwWMneLqKJRL1SWUBjraQAa5XMGz37Y4p9t/7YesFrn2Ds5svLpZNG90020ZNbBRYiYRV2xC07tSOF6NEe3jYxMmAxtp0i+fajP1k9j+TupQjbm7BThoHEBcEXyQlJdH/lLNZzwDXvlPaBM+SFQq/O+lqBvcZia5Pe463+5ojui7otfbxib8EC/HGuejQmqKUiKKp5VCWuzHmiozGIXjS0/0nxBF8I4omQRAiRiGF+cb7g5azGOyTzETKluKkyhaCK1YC4eky0pi9uQeZMGGCa7su9RDGALFqEh2jzVfE9sR7hsJBDWCWr2sxpxZ4IJ6qs5+jhYZBWXMO3a266MbHsOpro9peIpKI7+bG6My+s2c6qcgLfLwx1Vk7qcnYR8eOHd3iDLZmReKJhEajQaOJzfQmLc0eY+8Hhrn2Ga3R66suvSGNfF1yJfx/9v47yo7rOvOGf5VuTp1zAtDIGSAAggSYSZkKlGTlYFOyZXle2R57ZmyP5xvb83qCPclhbH8eR8nKWVZiEEWKOZPIGWg00Gh07ptjpfePuqlu6G4kEiDvsxYW+ladqjpVdeqcfZ6z97ML2mNlk92r1Uolyb5C0NPTwy233HJJ59DFHGn3JDf0uFzxQA+YG96cejRwydAccZyt1/YabaEuAHpah9i+fTvDyyr1AhtYCA2iqYEGGrhsiGLzJWbHuArGyFW3Z25gA6kCqmQP+Sv3VroR8WTykzW23uCTwQW8AEOO61PjpxKavFg9hapEHKpy468EmoJphYe+BWGUxR9Emg4hy9ebssLV/+6Hh4dpCpWynF77rIhXD83NzciyzNDQEOvWratb7tbWCJuCN/Y4cKm4+eab2bt37zU5dyGbcVjr5Uxml7UNeJzdfJn3Fsut7u695GREmmQntAWzFAQulF37amHPnj2sXl3SfBweHkZRLi0MbaZ9P4ngSURx6VnargSSYNLuubrPoZzcA0gJN5aX+9sd7oXzSFwRurq68Ias9tCcX8zo6e+io6Mk4REKhVi7di2yLLNlyxa6urquXYVuQFxvlkQDDTRwA+HSNZquL1Ln9ttvJ8HVDbO5WujoPIXDkWbs/MY3uypvGmoKal5fTeiSsZB+lGq64Tptj5eCx7mZ225rY2rqFsBKRTzZsnXhg24AxEPH3+wqvCFQnWHa26+uFt6V49oQzEPLV1zza1xteDweNm60jwuSJKHrJW9Xp9NJd3c3fzTwXUTg9ie3AFboRyKR4HqH0+nEMIxLEt9VFKX471pDl9P8i34XDsdPyYkeXmcnALe0J7htqI3n3Z20N7fz9GErG5osy8iyTCZTHdYti078ctKmEwlgSBqiwyDhP0OHdxmCcPVkAwohQJ2dnRw/fuX9mqIsLbTV7Xajqiqadnk6mL85PEb4tvdzSuvhmWefxeuu5fXcwNsJWvLSjUJJkhgcHGRkZKQugdvU1MSqVSXvJcFRKrexb4jHpqzvccOGDUiSRDweJxgMEgwGSSQSxONvL4K/HhpE09sQkiRhGMaSV0eCwSAbNmzg4MGDOJ3Oqmx+giAgiqLNyHkzIcsyN998M7Ozi6VAb+BKcakrdrogwlXOGvdWxcqVLwK8rYmmWrgxpoL1cT3rR10tnFDaAJjteK64TXV43qzqNHAZGF65ou6+7u5jRCKdpFJNb1h9FgoZvhL4/GUhdwLMXILQuyzLmKa5ZHtKluVFJ9ihUIgNGzbw0ksvkcvlCIVCmKZJNGrpAHW6shhK9bf00bXwyy37ioTS6tWraWpq4o/4LXawj2DAYM3wEC5/MyMjI+zfv3/J93k1IAolj5dly5ZVZTbcsWMHMzMzGIZlH9x8880AaJrGwYMHicVidHR0EIvFSKdLobGy6KTV3c9k8hS7du2qCge7WijYzeUheXFnit27d9tSczhXbeP5vEac2Bpgw4YNHDp0iB03bcfhdBWF5QsegwMtaxnaUJvUTTkz0H0KgJ51foxxmZRnHK+m1nwWBRTqWHiWtbCQx6LX6yWZrNZ2XOi8q7dtq3u+cuzcaRFyr7/+OrFYSbfvydv38dhUE//12OCCx+9pjfLnDCDJsHP9HbhbS9+dIOgojgy5rHdJdWngrQGn5EMUZAyz1Lc6nc6q/nbHjh2MjY0xMTHB0NAQvb29NDU18eqr1dmUJUli06ZNVdsLcEhuQqEQ69atK/Y5y5cvL+5/I8juGwUNoultiD179hCNRtm3b59tuyAINDc3o2kaqVSquJI0MDCALMts3WqtSFdmYFm5ciVdXV01M7M0NzcTiUQWHPCuNnw+H5IksXPnTs6cOfOGXfftCOkSiSYTmbeCx0YDbx5ucIemBRHHQ+P7aOB6x/IVr2KaAs8+84k37Jqq4+oILl8OejpaiCYzGIZJKmWFNymKYtOzyWQy7N+/v6bHClir3i0tLYyMjHD+fO3sS5IkFbPvFmyx9est0enx8XFOnDjB13cd5bVMHz9kh+3YCy17gFcRMBFEiaamEgn4MlvYUuZQuHxokJ1dJoWclkMhgT6fwdMX6veuwWCQdDqNoihIkkQsFmPlypWk0+maWeQEQWDdunWMjo6SSCSQpdLEq7+/n3A4TEhRWcZ5Rrzb8Hg8DAwMMDAwwCuvvFIsK8sy/f39HD58mDVr1gAW+XTgwAGy2Sw377gFSVCQTmZrkkwOh4Pu7u6697UYWltbmZ2dZc+ePQAcOHBgwfKViQhaWloIhUI4nC7ACsXp6+vD4/GgqmpdkqkSpqQy17qGtOcMSWBN8xp0XeeZZ56pKrt3715yuRyvv/46siyj6zrDw8McOXIEXddxuVy4XK5i+dt2bOTi+RLx19HRwezsbFGPqvAchoeHcTqdRVu/p6dnSXUvoFw/ctOKbi6cmOJs0g3Aw9xOR8cMlOU6Wbt2bdErZG5uDkEQ8MglwrmcZAJYtfo52trO8ewzH8c0G8owbxforjh3Dt+O1q0RiUQIhULFtnbw4MFipjiPx8OqVatwOBy0tLQA1nyx4OUpCAKmadLS0sKGDQvrdKWcsGLFirqE0vCyVcQ6Io05KCBc7ZjfNxrbt283a7GRNxruvfdeRFG0GSmyLON0OnHLBl0hD2mlifPnz5PLlQYyj8dDLpcrsraSJOFyuTAMg507dzI6Osrs7CyKohCNRjm0/zU+9xu/BcCLL75IJpNh966dOFzuqjpdvHiRbDZLZ2cnbndp/5NPPsnQ0BCRSIRwOMxtt91W9Gw5efIkuVwOn89HJBJh8+bNpNNppqamigNF+SpGAfv37y8aWAtBkiT27NnDuXPnkCSJM2fOFFcSPR4PO3aUjK/x8XGmpqbQNI1sNmvzuFIUBVmWa64GlT/bbDaLYRiMj4/T3d2Ny+VC1/UF3bkDgUDNeyzsi8fji65+ejweent7OX/+PJqm0d3dTSaTYXp6GlmWURSFdDpd97mV12HHjh0IgsCRI0eKLvN+vx9d121tp9CWzp07R19fXxU56Pf7q1xBP7Cxj9nm5TRw9bFn75cAeObpWjpFDTTQQANvDt7OfdPo6CiDg4M195mpME+9fIBgMEhzczNTU1PFkIrCxMcwDJ599lmcTieapqGqKp2uLMs23YrDfeWeGONHXyLramPZsmWLljUMg2wijDtgTbpMw0AQxeJC5LYugdFMkPbOLjo6OjDVDKop4nA4OHjwoC1878LYGCH1IqeiDjZs2IAsAqK1lq3rel1Pow/yI77Fu2zbJien6Oy8cuGVZDJZJEv0xAxrMvt5bNRgYPt9ABw+fJiu1iAuh4y3uYvx8XEMw2Bubm5J9uj1gEgkwvj4OM888wybN2+mv7+/LrE2OjqKpmmsWFHfW/GNxujoKKOjo8Xv4/Tp06xYsYLx8fFLJrJ23/JVJEnnuWc/imE0/CgauD7wn/7Tf3qzq3BVIAjCa6Zpbr/k4xpE0/WBG7UhLmRALBWJRAKn01mXGU6n02iahs/nu+RQLdt5kgnc3mpB2rm5ORwOB8lkEtM0aW9vX/SeLl68SDKZZHh4uLjt/Pnz9PfbM5hFo1GCwWDl4YAl7Fi4n2w2i9N5eQKEmqYxMTFBX1/f4mVzWWRH9XWi8zMEm9ts22KxGIFAdSafq/HOG1gcb+fJXAMNNHD9otE3NdBAA9cbSkTTRzCMRuhSA9cHbtT5fSUul2hqUL4NXBGuBuFQECWsh3JvqitBLZIJKLpQ+v1LFxWstWJUSTIBdUkmsOsbXS7JBJbn21JIJqAmyQRUkUxATZIJrs47b6CBBhpooIEGGmiggQYaeKvCNEyES82c9BZCI4i1gQYaaKCBBhpooIEGGmiggQYaaOAqITpfLaz/dkKDaLoOYKRieOODl3WsYNyY3iWCrlT9llUfrlQnmAJKtiRkWfjbkWlB1JeWQnUpcCW7kTQ3wbmN+COr8UUvLW7dnayOH/dFVyDn7J5R3tgQzpSlN+DINBe3S5rLVs6ZLolCKtn6nlCSamWcEXVH3RRc3tiV6SYJhkzz9A5k1e4FJqleQrNbCYTXXtH5G2iggQYaaKCBBhpooIEG3qpQE2/vDOiN0LnrAKbiw5Psx5O0Qq8MQUUwZUxBR1VixJoPo2RDBCJrybpmcGZbSPjPIBoKvvgKTAxMwUAwRQxRJeecIxE8DUAgvA5HLkTOEUEwJHQ5jarEyHqmkHMBfPFlyKoPU9DJOefRpQwp/zlb/STNhV6WktsXXYkp6KS9YzTNbUWX0kRarEwcoubCk+wnETyJnAvgj61Ak5OIhpNo80F80RW401bYmapYWWQU1U6q+GMrAdDFLKojgitTLQpZuOe09wKqEiMYXoch5Zhvs7KVODLN+GIrsHJUmaiOGK5MOyaWILhAnqAraFvntb2dmXYEU0TIc7CGYO0QTQVT0MEUivsAfPHlmOiYggmYiKaCK9NBxj0JgCvVVSofW2Wru2jKxXctFHJpRVcXz20IGggGpqCTdc6RDIzgj6zGlWlHF7OIhqN0XPG56CCAYEp4UgsLKabdE6Q9F2ma24opaAimZN0jAqJpdQ1Nc1vLnoNQejaaj7bJvaQ8YyQDZxe8TgMNNNBAAw28lRAIryUWOmpLg+mJD+DIhYr2kCfRhzcxRNY5S6zpKABNs9uIho5gihr+6DCxpmNImhtHphV3uov5tpffjNsBwBtbhilqmJhIutOyTUwJWfUCAqLhIOueQpcyeBL9gIAhZZB0NyYGKd85Mq4Z3Kkum13giQ+S8o/WvKaSDSGYMjnXbNV21Rkp1ivlO4cplhK6ONPtZN3TpfK5AKqjdhIWgOD8BmTNC6ZA1jWDI9vCfPtLZfvXE20+jGDIKLkQvtgyok2Hcad6cGSbbWWvNyjZIKLhQtKdKNkQsaZjuJPdpL3jmKJGaG4TmpzEFDVyzvCSMjgG5zZh5ucTmpxGq/FsRc2FIdfOsrgUeOIDiIaCKWok/aOImhNDzlaV80dWk/aME5rfSLTpSLFdNNDAUiBpHoLzG0gETpNzzVXtl1UvnkQ/Sd85dCV11a/f2rqwPMxbHQ2i6TqApIj88NSfsqZ1D6fDL+NVmhgIbuTI7FPs6N4OzeBOdTGXvIiR0Dgfe4Sbut4DwNHZp+nxrybobCeRm8fnaMad7kZKB4k6X2NyehxRmORC/BiiILGz+324011F0mPf1MOIgkSXbyUhtQmX3EHGPY4ha7hSXfhjw+T0NA7JjSGopHzn0WMOFMFFINZKXAsTdHUQCK/DFHRcGcsrR1H9SJoHAQFZsz6ytsm9xXs+F91Pk0PmaPgkXiWEmhDIOeaZy4zzzuW/AcB45BT9ASu17wvj3yarp7i9/xcAEBARTBFvYrD0HHU3nkQ/Kd95ZNWPngOHbGkSSRlX/jiLYMpqKeYzF+nyrWDf1MMsC20j6GwnlZlgNHaavsA6gs52RNPyvBqfP0NPWXa1E3MvsKrlZgBmkmOEXB04JEtLKqdmmI7MM5ceY0NbC4pU8lyaTZ1H0WIEA+uJZWdxSm6cskI0O40iOrkQP8ZAcCNOyYOqZnHKVsYUd6qHZDRNTE1hKnHcit1r6sXx79Di7iWWmyGtJUjk5nHLfnyOJjq9w4ScLbQo4zw3cQK/q582zwDpRByH2MahxBNsbL/Lej75lLCzqTFGIq8RdHagmxqrmm9GEmUeH/1HFMmFQ3IzGNhIwGP3ymqggUqs3/AYLleSV19575tdlQYasKG7+zjLV7zCs898DNO8Mb2DG3hz4My20ja1l5nOpwHLQ9qbHACgdfIWkv5R3Mm+Utky+6dltpQdt2nGg6R7qhaNoER+ADRP70QynGhy0lrwMkXm218uK7uRnHMeZ6YVWfUy2/lccZ83tpyk/ywIpUyyvugw7nQXhqAy1/ECAJ5Ub3F/JDNJyFXysgaYTJxm7ML3yOo5smYny0Jb6fYN8fVnfoebtzxIn76C6ESWFn8fsuZHdURwptuRNDeqI4rqDAMQCK9B0t2EW1/HGx9C0fxoUhJJ9zDb+QwAqxJORjMrSPnO4071oClxsu6ZImm1Qx3hWWcQU9RpmdqNaMok/GfIuKYxJRVXqgN/bBUmJrqUQtZLGf0Ki51tk3vRpBSS7kZAoHnmJkTdUbQTm+eqdW8D4bVF0nApkFQvjlyItHccsBYffbFl6HKanDMMmCT9ozRP77C9zyqY1kJogVxzpjpAsMhMWffYirZOW7ZpoT1CaUHXk+wnHjhBJr/YLOlOsu4ZXKkORN1dJAQd+fLObCsmBrOdzyIYMqH5TdZ9aS5UR4xo86ElP4ty+COriovIJiaGoOFO9WCIOdLecQRDRnWGMcQcjmxTcW4RCm9EFzN1npX1DV1BzqAGLgkmTmeSbPbNJ1KCcxvJOcOIhoNk4AxKtolQeIO9TGQdqhJDVeKYgkbKf46mme3F79+ZbUMXs6Q9F632ZgqE2+onG3OluhBMCUe2mWjzQfu15tcj6i7r3L7Wa3LPNwoaRNN1goyeYN/UwwDEc3NMJi2PpCdGH2X/v1SnsM/pSZySm7PRgxyZfdK2r9nVy3zmQs3r/ODpL7Fty034HS1EspPF7SfnX7zkOu/fX12vy0X5ub51/I+K21+Z+D5BZzvhzAQAPz7zfzBMjYyWqHmeJm8n3QOtnDj5LK+++ip7d9xDSouhGdWrJOU4HX7F9vvY3DN161fAwZnHFr2vM5HqTmqx53Zg+ieLnrfWucbiR2z74rlZplNnGYm8bj8odrzqPCfmLaO02dXLEy/+qKp+R2Z/VnXMWOwwdzV/eMl1beDtiaamycULNQBA08w2wm2vvdnVeNugr8+axMtKFjXnWaR0A29JmFCD47HBH10JpoBoOBEMiefOfYuZ+JP4Ha1s67QWw5yzMzw9/SKJXBiX5OLl/S+yffMulgU3IAgKfvEEWbOfU5FTzKTHbOcXENnScS+K5MHdaW1rm9zLvqlH6J0TmE6eYE6RGTl/kjVDmzkTeYW7Bz9TPN6RacVIyfhyy4rb3Ik+zLQDnRwvjfyU/qY19LesRlFLST6+dfyPuK3ndjzePryqwA9O/W/6Ams5E34NE4OB4CZyeprp5Fl8jmai2amyWo8zP2mRJ0enxnFM/pjXyrp6t+yjzTNAUo0xlx7D7wmxe8O9+KMrERBR9Qxtk3t58tzncUouJNFFRk+x3bsXTJH3Nv8Wv3v058manTwx+0dsDBnIgWFGwmf45eF2Vjm+zJ+/vIF39ye5o+klDER+OqHzyd4IuubgaO4ufhbLcS56gJ+++EO2btpKt38NY7HX0cySV3qLu5dodqZoI4qCRLdvFbOp82T0BH3+dZiYrOi0iA5n9tImjcHIWkTdhSloiLrl8W9o55AZRNZ8zKdHuI8ZXjMWloVom9pLynOBrHsad7KHMyefxhQ72dyxitcmf8yPn/ln/uCD36x57NPnv8xUagSPHOT2/k/iDPfjzLSh5IIISOTSHYQyP+GE2Y7LL+NMW8lh1ihf4psXe3BIbnLxBKMzx8jkfopDdOOUvdy5+qOX9CwAQnObiqRXwdZXRCfvXfm7TKdGOTLzJHcMPGgVjoNbGCeS2Y/TeVPxHJLhQtQdGFIOsOQpkoGz3OBJ1G849PcfYmDwAK+8/ACZTO3kQW8UHGoIhxoCqIrm2DfxD6xt2cv5xCzDzbtpSX+VmPyrRSL2Zvd/5C/ObGU6dRbN0Am5OmlyNuN3djNo3IwmzRNtPWE7Z3B+A/e7v8CjkyLn4tP0mb+DiUms6QimqHO/8mv0BN18c6wTuOuNeATXLRpE0w2Ki4lTdffVI5kK0E3VRjJdzzBMvUgyAaTUyILlw8lJwkdL9xbLzVyrqr0lsVjbqcT52EF6mqtDG28UyDk/miNOILyOWNORxQ+4wSAYMqaovdnVuKoQdAVTUt/salwTSLrlFSkIOj7fPPF4dTbIGxXNMzcVQ5sbuDFQPpl7y8IUbZ4+BQiGgqy5WScc5c/3PYnP0cLPLfscJ+ZfYCZujZPxnBXuZRgG3z9bWnhKqhDPhAlnJnitzH6B07WrgMHrU48AcPvK2wH48em/IKVFOR0ulPop+0/sJ+k+D8CR2adITswTcLUwlzzExcS3AWhydZNSo2R1uwDtbPochyafJOBsYyCwgWcOPIy3E2ZTZ1mb2IuHQ2T1pG3R7Vz0QPFvO8m0ONJagvOx0pgaT0V4Yf+TrG3RGY0eZCp5hnbPYBXp5kl+HIBPH1/B+dQJ4AQgcDAiQWQEgEPTr/G3c0PEMxpfPenkq6RL11UT/LtVY2RyJ/nji5YUgaqrZI0sZ6P7qZSmnUvbbR7D1LkQL3ksFRbwVmARTScvvkpT59Kegag7uTC/j4Hgbvz5KIJ09jA/OvtdWt0D3DHwixyaeZ7fH/oOW33f4o/5fxY8X8Hj3BQMTkbHgDFO5d9XVtf40ek/p90zmK/3UTq9y9BNnamU9dxSWpSHRv4KgBZ3H4ncvOW5L0hMJs/gcIyys+UWTp/dzz+u/Q/8+mNwXNlSVY+ckSaXSxPNLN2+9pw7x4GMhpCY5uaeD/CTs39b3KcaWZ4493li2WlUI8vT57/M6pZbuRA/ypnIq4iCzN5eD93iN/jqOQlxNs7mlUECG28DQNaWnjG6gauHUMjq25zO1JtONPUa/4Z72s/xN2P/itn0Bf7mJz/gtp33ktMzpLUYp6NfB2D/9E8B+MTKp8mKe3l57M/4UUYirR8unmsmNcpMahR4ndcmf4Rjah+7P/xbtutJWT+fPVjQXnEymvkusewMkiizLODiV+dXYeZXMD59rW/+OkeDaGqggQYuG4YWBzpwZJrJuebxxCVSfn3R464XaA5roNDl6rjsKpJmCSvf1wtE3YkhZdmmnuNc+KPF8I7rDwY9PceZmFiJYSw+HDXP3ERImGOkdeQNqNsbC3eil8dH/45NnWtZtvxVurtP8uorD5BOlww4wZBsOiXXC5zpDrJuayIanN1ItPVgVRlFr/3xtLaeY83ap3nu2Y9gGErNMm82mma3kXFNISCS8p1/s6vzhsCr6WRMBd7iRJOmq8iihDvRR7tjH/HI/WTdMzgyLfx84I+JZUaAVSRyczZv6wKee+45zKvoSnHo0CEEQSClLayjc3T2KeuPimLhzMW6x6hGhrn0GHPpMU5N7mdz52ZORCdY3nyRpy4+e6VVXxSx7DQvXvxO8XeBACnHgXPPoDmjnE/VD8v/1oX2uvt+PNFCOCfz/Fz9hCqXgwMHDqAoCj95+if84eA/kvKdt7SOKohKR6aFnGuO0NxmFDXAzyb+C6PREW7qejceJcjT+ec8mz7H//vtj7F2/Wr+1Wsrmc15WHfzwnVw5KzEOAWPo0qktRjnYqW+92LiZN1zzeUJvulUSUsrl8vxzIuW9/rt01vYf2Q/CwUtHJh+lE3dVihd0+w2YqEjNj1XG3Sd8Qnrmt898cfopn2xaK6McJxKjdjahmFqPDn2HQpT1tcvjGC0bub2/P7+1Ascai6FRr4V0dNzlP6Bg7zw/Efe7KoUYV4nBrGS9fNnJ0P82ckQ8HxxezQ7XfeYL598Ckl4Ft3UgIXD5l+eENhd9js0Mcu/nPzvtjKF9qsacHQ+wQ0zWXgD0Mg610ADDVw2Nmctd3NJ89A2uRdXauEOW8peXzoohcx/jrIsh0WYlQPFjeOXLWmWZ4xLtAQ8m2duIhO+/iaMbW3nWLb8NQYGDixYzpPoZ1CN8Z7A/+KJs/9wxdctZG58o1CZvbEAS1DXgi+xjHDGImt8vnnruAphVOEaEDFudxS3O3pFzbs8g6aYS+EZqfYOfHjk87bffVFLlLM//+5drtrh0NcDZM2LL7EMT6IfZ7qd0FxF+Mypa59VRtSdV3R8zUyhRm3SUlI9PD795BVd71rDH1lp+y3nApiGNeF3pmt72bpSXbTNDxOc21Tctv/APnLiFN7EIG3+x5AMF55kH6daDvCvX4Pf3D+8YD1UVUXTrp7X6NzcHLOzb1yWIs3I8YMz/8B0+vrw/v7mC39WHe5/CTAReG4udNUnweFwmOlpa+LqyDURnN9IYH492RNn2MAxWqZ3EZxfTzCyjrbJvShqgERuHhOD6dQIPz7zF3zr+B8RK5v85jSLlDkW9zKTrZ6ONc+UwsV0Q+Nu7z/RNrm3GCL0ZsPMDxpKNsgWx1M4L1g2hiPTYtMkA/u0u5JkulJMzlR+L2+OrZaeXvp1jcilTb+XLX8NWb4+PblFvfZ9N81us5dTqz1HrxTB+Y2Ioz+8jCPNPMm0NIwcmiQQXoMr2cVPRy5imNffgt/1igbR1EADDVw2SrRRbaNO0K0uRtA0BE3FM/9m6KCYDAzuw+mKV+0Z8V5A1FxIWnW9qsRZr7Ht4kx3IKkems4t7DHhSnbV3K5kmxAM640UXOwlrIFd0t3Mx66/7l6SrIFelqtJsDE5jpyzvHnud32BB5V/RFOPMZq8/PuQihEWb+xqU+G9VKI8mYFpJGnTKowXobLRWe9zz94vsWZttW7a5WD7TT9g+00/4Iqeia2epfOUE3rlYczuZA+B1FhV+asPk8HB13E4kguWWmoNBEQC0dV0j+8vbnONn0GfHKt/0CJonbplSeWC4fWXfQ2orS0jaGnbb1G3PAZebn2NqKpR6PR27voWW7dejjF/9XDqiJ28dGRbbL9FI0ehvt74AL6onSBqnbwVf2yY213fw6EGkVQPx44dI5FIoLY8yt/e/JscDZcya4mmxFxOJmdcf/1mA28upr3nERDYIB7ghclz/DyP8OmWz7LCLBFkA46X+Ono31/CWasNjEIotZwLcGDqR6z1WGE/zfLolVT/qiGbX2jcJj/H1049Q9NUITOfdS+OTOkbFfWrv9B19OhRkskkml74RpfWk7sm8wukqhdPfGCR0ktHND/GKbkAG/aJuFILyUrUr2v3WLjuvlpwJ/ouqXwBwfkNiHpJG0xKL1B4AVjZqqsha16UbMmz0BMu3ZcSvnJi2x9ZiZIL8ujEtScWz88dJxcz8ceHiwRrA0tDYwRtoIEGrgp8yWp9J1nND+ipOL5TB5By1WTPtYIzbbnXu90x+vsPs3btU1VlXu16iufaX0Io6wq9sbygqlnRPV5jpUnBtLLc9J0fX7BcwX2+9jmsiaI3MYg70ceQUVo9vZTpvCiqvNkeXC/3lUTx/bK1Yrk/cmXZTdwX3iy9qvpPf/bMIdznTpCN/i1y4ZFXedMVTlN6J62tS9NTq0dyXU0I5RnbTBBVa1LhTvVUf0eAL76cnJgulgcQqki1K4ffP0tf/xHWL99/1c7pEecJxM8BIMfmUWLhpX8qFe81EF5jf3aLHNsyvavorVgJX2RF3UOFumGp+fqYInLOz+OdzzBriow2HbOVcjgyeH2RpdXzMuGfs4c5SJrde292qlIjyP4sQzE3Rn68EUwZV7qzorTVDrOS5UWSUCaZyp+zcKbOsu5lLHi86hoNNABwtsXywvRI88VtXinCF08+yXNjX2Cr9zvs9f9P1EWS0FTibqPaRnEFv85e8VFGogeIiCLfX/d/+MsN/3xlN3CVkE6n+XX+ifukxzgWdyMY9vG1sOAlZlII2tX3xpmenuaVV14B8dLGOCFZ8IAy8ST7FyxbgKgtnmG5EEIrGDLe1Cx9F+pLFhgLeNPoZhkpV88WKIOs+WykXv3+3g5HrgkhTwD6pluQLts8Kg2AzmipvqaZQ9Ktb8A1PoKULd2XmK2Wq7hUuDKd+QXhN6affuHi9zgTfpXkIlrBDdjRIJoaaKCBy0bB68eRizF05rtV/b0zk8A9ehzXhKUDYLK08Bg5UzsDy7rDh2mb3EsgvGbB41snbyEQtYRACxNYsYbgKwJcDJzmlYnvFzc93/M4j/V/E0OfrSxaE0p4HteFMwD4/TMMLaufDnUhFIbqnoiKlIzVLaeotcm6UPQMjqy1b8Ohf6B9ykUsU/KOas6UtBO8pw+hlHmXSfEInjOWGKKipLnl1q/T21cSR7xUOGbGcU7V9vIQDKXKEFKyoZplIy5r8mkC3xhr4/Nn7d5cSrg6Bn/4xElCs1trV6wOIRCarRY8vRpwTI/jP1ZqD5v2HePeRx4t/t68//8wcnEGOWW9t2Ibq0O6XApp1J5f5RNMucqFvRZE4/Ld2stXRQUEBENHvfA87nQXK0542TD5g6pjZMEiay47xKVO2FfN+oml57ky7qUlZidlFuKJgnMb2X/xL4u/N3geLns9BaJmac/OnezGfb6km6LkQks6rnAt0XAUJ29VmPgGznTZszTBY1rvJTS/seYhhdtont1G0/wW5n0XeWrZt6z2J0B2EZ2gehB1J90j9cM82yb34o0P2ra1Ttk9OYWKCVZlK+k/a8+wlZp7jPT5IzjTbQimVO2RCgy7nqLL/QJPLvsar/WUiOxCSRO4yfmbPDb8BU63vUarptGkN0IkGrCj4NEwJlttfMNQPx/t7iBniFxMnufQzOP82r6VC52iJrZTHUL+lWaFzw1fxERgWpKYCJwh7pqvcfSbgxaipS+tzmKcY24SU7iGksDxUdvPeosWkuYhOL8BKVqyoQQEhGwdXanyc5b1J6JWO4xZN/JkmmBiCiLmAiRRPFd/oUjL26qy6qN5ZsfidTMp2rsArdO78c5btrYjLtJ3pn7YtaRZhI+YSy15vaTqeZUdaOZKC6XZyF+hZC3NOEFTCw7Z1nUzKTyZpetqNc3chKCXyEpRf+P1HKPZKV6feog3exH2RkODaGqggQYuH/lxtGfiefzREaqmA6aJnE4g5CexcSWy4Olk1RpIPHUEj4W8IaPkmhA1iY7zo8V9drKieoA3jDrGhACjZdl1Tre+Tsz0LHkocczPosQtl+DNWx6ht/cYhYGochWs0k07NLe5pnu1lK4f6uOP1yZwJD1Lz0VrstYUOc+uV/4LX7xYcluWxBIZIKpZRL3U/XsunEbKZfCMHMHhsDxN2trO1a3DYnDOTiDnNXgqoeSCiBVaQ0Kd8JS403quIiZ/c6aXpG4nWpyT5/Ef3186j5pj9YnjKJrd80nIZfGePoRp1vOoE6omvlcDYq7Q5qz24E6maIpEmHTO030hQnPkhM0NW6hsdWWGqnNqDLEO0SSpXlv4pxSP0Dxfsupkrdqgq9RTE42lLWc60220TXbbttnIj7I6G/O/z5YDn+cFR62Vffu9Gon65GpNGHV0IWa20zRbX1dnr7KLO8SFV7FdcWsi50p241BDZNUpPtP+MdZ7Hmart5pQF+oQTa5kF6EyPSAAeQESeSEsRscJmPgSpVL20DJre6Ef9kwk8Z6fB6FAlNU4uwCauTSPDCXThJwutYFAeB2SZn8/lWSyUqaLJ2muKttdURcOd5RqEI1SMk4guqYmydQ89Dlmez/Pt8dbOd7xok1UX86PK7og4BZnONO6D4D/PDvHf6vSf2ng7Y5Cn/24pzTOHHY6ialWn/rV850LipnXQ2X/fzZW8ohRO1T+14k+RAPEqy91c1VQJGwFE0/yDcxsrS/Rc8wULC+ewvPLV9g7Un9RTclVh/yFZtZZh6v2fS61ZGOaQkUfVGbjtE7eCn7L1nVMj+O6eNZWtNAKlGwTrkz9kMPSAo+AVEcnTs4INk8zZ7IUQr38zPdqXHVxiJq9TpJt/DNxnztR1GnMSaX7ltXSNcR0kpZ4m22RCrCF2pXDl0gWF3SCF3voGF+35Po28OaiQTQ10EADl43AhGVM9FwYz5NA1uDa1XWCteueqDL3Y0ppQG+e3sHQiD3rTFM+htst1w6R6h2zVoFEU8Z76iWi+cmCHE8RCtdetb8c2BRn8pN7z8SlxJRbZ3Cm22wT4spwDkUN4FWrA+PleKTumQNxu1Gy7RVLF8IUBLa+9jqP9j6KK6+dUK69aEh2cieYrKEJkU1XGU9LRSCzsGGtZEoTuyq9mQqjrODx9FrbaxwJHeFfPVOtLwP51lY24fSdPognZX+eK4+IeM8cQlSzGBTeof3eBViyCz3A7px9tVpYZHXN1CzPK0W36vZc98/Y86zl2aQv5BpfvjJrGNSjG0LzG2me3V5x0WIc3qKntn4vzdAMRNfgSZW+T2eiCSXtgXTEOk9ZHTVSiKZedS3T1JnDHgJm6AsTXXJuafpukubAiFZ7BpSjcg5QQEvLeTZveQhP1CIXCsWmcxL3DDYjd3zR8owsPKt8AaHsG3dOjOJKFb5zoRjOWqcmcAmCpEuFK9VJILKm9OYL1c3XW4ydREyW+l5dGyOTvfysY75pJ+7Rl4u/Fc2HWNGepFwFsRnPZx8zREJzW6uatqSVTR5NodpLwaz+hhdi4/5bp8wftwXJ6tVmb6FmugDzWR9bTl7djGUNvLVg5tuigGBbKLhSAXIBk3/NPxZ/n4qWvO60Fo0Lyww+/mg/H3qi94quc81Q9k2mvJ2VO6/ZZTVXfTkBACVnD9urHOsEwHuq9pix/Izlrd7SNsKmzQ8hGJZ+m/fUAbwjJyquU7KdTMEux9A2fWvZ9US2j1vGmWBoKBWLciXC0WTVya/XrJf7/Kli0gPRcLD7xT+oKCHU+MuO9pnXS9cSQNZLRJm4QPidmC2Evdc4swlyKo6UrbBrywZdMZNCADJ6f/FbKtVVBF1Djs3jmC1l0My4WzHydTWNmWsybjZwbdAgmhpooIHLhica5cNf/wbNeYJIzK8srRh+mZaWaq2h8jFFMlxIefFjT6Kf0MzG4iqQ07RP3EOzWxg6NUgwFqP3whNl57MGa1GrXOITMI36yoZyZLZmZqxyuPMr8k1zW2mb3Mu686etMy+QYajgKl2YFJn6LGLZKtego7rLNSoMMBOQMvVX832J0uDbNLsVd6oU6y6aJgklgQmokoheZmJUmnltS3Cs6LlwIX+dbbznX0rhhY7ZCVwz9hXLoGQRTV1dJ9j82WPIDrtx1zRdqmdB5LQ0ES7Vs2l2a5E0yUpZjjcdr/JkKsdi5mvCv6n0FC5DhKC1bZSOjtO2bav1HttvYZGh1MT6Lmp5DalGmQdQME+a5Z9Hd/nqngCiUU/nwm7wKbH5etF3Rbgzl++psf5IaTKkjD9BLvp/MUef5b6nHuam1/4YsCZjlVXoT+6jaWY7Wvpp0nP5yVL+Xr2ZhSss6laD9cVW4D19qG45I3cKI3fUvk2bsBeqMFILT2/1mmfw++foiOW/3/z2D8bibDsg8Kud7fzzaGdVoyv3aHJEZmmbLnkD2ldsTaSKEBJ35tKEXxdC4YlLmruiTdbLoGlt15KPQOplqrG0ibNQK4tU2TN2nzuBPPVK8Xcm/Kf0zViTMslwWgsHsUjFlUsPOTi/AbWvNIlzJbuqiKzK+q4/XBJiHhz7WvHvVufCWjGvzq1i0+nQgmUaeLujRDQVmtw9L7ezPXjpXkzl+NzrK3noXImAr2ziY0MGkingyl173b3LQdGjyUxhaBdpmS/1w+IbSAz0jJXC0tqmotz67DP2AjX6DrGGhlTg1BQdFy17a9WGnxIIzNnL5xe5lMgM7tHj3J3PFmlSIJqs62jpp2id/eqS6u6YvoCYt4tFU667KiKlYmx57VkCcxuQswpg8s4f/qiYqdCRi+XrYiKXJ30oMwwE0yg3wnCVjUXvV0sLV97TB5Fj1r4dN32LLXf+rHif5X+I6SSOCjmDclKv8rEr4mzZokzpVP6T+3GPj+CcuWjb54hYNqeRfIlM9glWnP4uh5o/j+m6Tl38GgAaRFMDDTRwBah09Q5Ejy9cvmKgWXnqJM6MjjvVjT77T4S9lqG2Xu/jFnUVYqZETvSOW27khZAKh+hGSkRxTozinI0glHm2mEaSbPRvKq5eurh7YrR6xSWPjjkrnl2qXK0pDqZpWidvRcqH+ZV71FQOpKaZsWmyrHHJiBXnzVX0wgL2QbNzzE7oFA53ZVpYKQVq3sOpziYeWz8EZatqc96FibXyGhRwy7PP0Tp5K7LmxV2m8eSYmyBQ4e7dYVikSEenRco4/HbDTdYsI03SM3ROvFD36m0z43ji/7RgDbe//ErRxbreVNid6GXN0aPYWIFiAzTZ9XyJrGgaeAnFW9tjbc2aZ1i5ql59DUISBHJWmJKYTiLksjQZS9ce+OVwSQcncZtdw6xZL/PsM0FQqzWpakGJzVeH4VVA0u3vp55Gk+/EPgKn7SuuvmS5AWh5MvrSOUITseKqvmVs54nXfFUcZgZZ92DqM7ajAVZfLF3/vtwmdh6v1CGx6itqLkTVHipRFPCvQNHDoKKRKNrCoRaalL8HzRKL3h3P8sDzLnYfDuXvtkBElav7AHmvrGCs9G2IpsLaw6WV73L6TU08ypZ9f1azDn7/DM3NY8WQCDX5Eww9ipkndbq6TlRl06ty+ilcyzTJJR8qGf2F/xbhkU4lD9syB9aDSXXbKW9/cioOFSRrVrJX1puIcNvjJRFyrZi5qhVFDdreoT8+jKsWX2SCrlrhw+2z+4ubU7nSwR5Zz9evgQYuE3li2Z+iJB8w66bLdWU6RKcSHv7+bDc3qS+yhUNoSxCCvp5Q6F9MfYZc/OvIZeGvkra4DtLlXzj/f75LWXnMsnWcmQx3/uyRKo8mM9/3hFrO4W49Vf+8RpyDw3Y9PwGDUD4cTABCo36cE+eQ0wm8eT23QGgCcXDa1h/PYSdNChCRkITSwqpzbhL/7Dy+6ArcyV7c6eoFoQ9kdyGYJtOOs0iJMbLRvyHhUPAlk4iag1ziB5QcN03ap0s6kbblB9MsjtGGNkdhzWtvbi0hs2TDiGquKAPgdGdwBio637ywt3vslM0OBwhdOIuUk5DSCTQh3x7ytoZPOY43McgtT42WHVHbbvElLtA9ch7f8dcQDB3dmKH/wuMMnwmzcfk11P9q4IrRIJoaaKCBy0b5QCoAhrjwanG52dQ58QK+eIJNr04hGvY4bdEUWKP3lpgbU8UURF4f6OBY6BTrnBrvG/gNHIIDR2QWwYTuibLQD6PaI2ipJtvPvdTJ7YfVsklyfhJV+I3lwWKIhTOW37O1rT1RmMwItpUyAYH353barpeWbIdWZW6RdXsISdhjkXEhMc0Gt4TkqQ4rmwxaBIWEyN5n/i17nv136EqlEHvtAV1KJ/LVMYl6nOjZGvoFNQ7dqPZUlLEXGpqwvEqa54+z9sSXbfvksoa05cD/IZlcmBRbPjJCcG5VzX2jrQG07CF8iWVsPHiIgfOPlarksp6laUTpvXAKdyIBGPRs+zoDd/6x7TzOOq7j78qWhLUHh/az4ZYv0Zb3kpATEXxnDtmMtHLUIn+2ZrNl++2QcyXjXInMosq1h2yzxgqtkF74GVaFD9QLnTMXCQjJH/aukxXfftlBgglaUGPck8HQIxhatRCqIJdW6fuMVtqytYkvoeIPV6rTym63AJyiPUxP1uwZbyrvvOiir89j5lfiX13WxcrzeQ+zitA5tSuHL7Yc76hFlDgq9IXkSmIrf7ihnkQTamff2bzlEdatf7JUJ3UMLf0sphFFUdKsGH6Z9Rset5+2jhuboU9g5I4jpgt9gFUuU18jFlMwudAzSVOZsP56rb9KpBsM+m//IZ62EnFvmhq6aZ9YVhLole1NNkDWdQKBKdaseYpcXojGnbIyCykL8KaBc1bZVocPNfFtDM0+qYuoobK61T+PUB41fO0ifRq4wdEcN1FTz9A1uv+aMJbf2Bdh8sxREuWevNeQczIl87JTtv/6vmH+5Hg+7LziFGlHaQxdbOHjUqG1aIQ/FMZwGFV9iZEfS2qNaYY2Rabd6nPXbnqEgTv/R3Gfa3wE/0UvYl4j0zQibGm503a8IIjcHiwsAJmMBw4XX0371DR951KsvelbOD/47ILvrPA8NrfcyXv7f71qvzvdjYCIL1GtyVmwL6ZCPgzVWtTI5gl0U5/EUE+TcpTIq3KP21bs0gjuvDaimT1N0nl5AtuO8DTe0wcRa4S/O1MJghPNCIaBoUbxR/pwj1th271nnkVAoGW+tBhr6LUJOR3Nsr1rvNPB2aWF1Tfw5qBBNDXQQANXDRnZPlA5MvbJxp0HSoPE2hNfZqzZz7GWi6xyZPnw0O8WBaT9Zn7gKIrVapiCyGTIGuC784OoLJbPlMrCJg79bXXlFskM9e7v/4D2I68BsPOEwLK8ftTu5/8rN9vi3+1eAQ5fFkGyzl0InetxDxZLO8omuQJUkRBahTHiUGtn5nPNG7RN7SGXN6LEvLKIUMu1Ol83p2ASdcHxTi9bztcTwrZDmbe8OPymm+eHe9FSP61Ryj7Yv09aVrbNHj5YgJH3rtLF6vr2KXaicUHdIiDidqIlflhz39GeNrTUY2iZ1zjf7Kd1ruS5VHJo0hENDU0Si01Mctqfu6eO7lKbGaBwjy0tlhEoKPXDNC8JVSFZJaNwIUO91tMaba4fXjGgt+GL2WMnHTVETwE63UP0uEuaIKZpMuMrI27yhl/cqZCTRCIe65s0AVMpff/R90f58vJ5cvFv2Oue10Br7bKLe1YZlAVuR55CcpWFZSX7aoo/l8OnhGy/K725Ko8uGOYmGcTpP7ftiwoioajlhabkwwlyHRHcqR7EXLWn1M0v/n7Z+cpCbvL39NLy7qpjFoItTE+x96+F85tmFm+Z15mhWmHMzosjeM4eLfM8SNnO6cyJtERK36JS0Wfu0ob5pax94uVyJQj0nWXgrlKodC72JZR8BsySRl299msiGBq98zqCabBu7VO0tp1Hkq13pKvn0dIvIOXP43LFWf2hz5BeO1U8g6hbz1PMf8y6epZMGXEpm/7i32otqzcn0xRT6D5RGk9uLF+SBt5IzLtPoWdfAYxr0lBOt4p8OVaxgHSNGqQpm8z/wjzprZc3hh2K+nhk0vLoNRT7mGOU2SZXm7dNbU5heA20znJyw/q43YKH/rMj7HrGCpkrH0ty8a+g5OwLDwUosXmcyQSe8yeL0gqKYGfjqzS+y7IZC0DvWO2Fg0oU+2BMZLHS1jDR1XOYpoEAdTP4liPqts6x9sg/5etSFrJWVukNyRbbcf0njuA5e6wmSQSULU4sBBOxjranYLkA5+sE3kRnUUy87fQYdz75OdzZ8vBxu0eU99QBPGcOE1a/y5y/QSjdiGgQTQ000MBlo9L2Sbrsg3Jw7Izt9+G+Ntvv2fzAsdJtkS+OyDytk7fiJR++ls/QpEa/QVJRCSbru1+XD6auXKS6rmWCvV3uZfiVZuuaqsC7O3y83tuMlPcuUXSTtUeO8sFvfBNPJoo7M1c6v6ljpvYXB/L1Hz7O0H12Dw37xNess90OT2qKO578XJUOT2X2EqHi/wI0UWSsqTChsq7ZLOi8tKKHsZYgPZHaXhWVKGSmkqlNVChKhk2fPoarw3oXHm+Y2Vv+X8LLf2IZLEWhSPsFom5rAht320mlWjCdC5ulry7rQtdLxpeYSeKcGLWV0dJPcbivHdFQadswx6qft2dFXGyFNajV1tsonEEJTyMaBeKg0nuj8If9LR3qaePVwUqR1BLEy7XGTRNRX7qI+x51NRea7IL7A6OjAAiCjqCXKrK34/3sbtld/G2op3jFRo5YZQ/0t/Pi8m6O9lrfuClYu7JyRTsq83TZ/dhTiGqeJHDbjcjy1XAoPdMN7/pTNvziqartlfWp/LOwwRBzVQcJeYKoYPyXexGmHSI/WT9U/H3TAScJWeCD3/hmWWbFHLc+U1qVFtwlY96dmUdwW2K1hZDB29W1BA0PYJJV7PepZO3faen7N4vtySxk+asgc03DmhQY6ig5o3plWDAMpLJw5I9+7xlcF84g5EM+3vFiB+9+vqu4//cfvrzEAKYRxpOaxH3+JL5z9pDU9o4zrP346WLdBwZfYM32f5u/HaNK70nPvoqWeaH4yvx+K5QkvbV+Vis98xJPrBss21J64QeblYotEHh5mAee7aZszkjNRIkNNADEnSXNN7NGp20oRl3vwsVgCibxu+JE3h+53OpdEgyn1eizK65Ggy8R6FDWd9XLvnAVYComZrtdg08Awpl5jnT40QUBrZj5rHY243IEZp9DMHQcJNjwqRPoTXaJAMU2bdYRK0LFKj03C0iXZV/LhP+0aB/VaiUxp46a+A5q4jtM+T045qdqlCrAOsNIRxMmJdu33A+5b+CdeE0n3RMRgp724vY5r8y8x1mlCVo40nfsVTyjlhxGuYxFJRZ+vRX26yJJRwo2/3BgK+/o+SVETUXKVdv9sro0Qq+BNx8NoqmBBhq4bNQiO8ox46+9cuTxhIn+vEbYWyCmrMFHFATU+PeK5ZzTFyyXXE1l3pUqXq88O0bpeJGA4a5dMaxMIQXs7fwg9/d+BgB/0pp4xDwusvkVORMZAeyCs2UTvmz2CZuBEOirDNWz9hp6JQFVH8HY2SUtWhYMiErSKuJ1cai/3XadO46XjB7NaR+YL9eVPRicQpRNmrdaq1CuvCdQqmkEKZMsalv1eofxnjpYul7x+S3hLqVF6lb2XgRMvGeP0dF2mqbhaFVR0VDp2T2NuzVbXLUTs/WNFEVJc+/y07Qsoi/pmjyPXFgErBKXtu4x0BmhfVNJ3yjlVJgO1tdv8qUWJ+HqYev+Sq2f+s9QRKwiMOfz+mi37vkq6wZK2Y4q21nP2I9tvwt7DUEg4S4nmk0EbRmPrxtEzIeAKJqJ7M7Sd9tFBNHAmYuBYXm+yBXekONNfq42OjpPc+ruX0H1hG3b5VilHpQdWtkk4YXhXvYNdiKWhRQKJvy0jIxyDN+b/8t6B87ldwNgaBcQEFhudPL+7I6qL8F7+iArj9pDHg1boQqjvWKSU9LzMIlrpdTo9ea8rmwWJV56Fk0Jq/0FEzLJPUn6Z2Hw3MO1D64BJZNBjlh1yIhh5GSslKgh/82uWvU8Dp9K2m/py/UsO4yxIkVOEjncoSHmxdJdkv07qRLct3k9LtaX1fescBoGQoG4K/vm/+QLi09KG3h7orwN5YbsZKwpmIQ/ESa5s35CjwUh1P7bcF9jweOr4HIk5b1ixVztxaZLhe7X0QN1vkMTJF0guEZD1JI4pi+gFAl468FlHAovrOjhhRVWaLVpVNsHlSgs9Hna00gOA3XZk7b971S32n4rFSRIucfpUu6/Vsiimrd/DG2M15Z12fat0eqHiddzBPe1rOWj2Vu5v+nncZf1q/v7Q8Q8ZQtqNRY1izZ3PFwjCcfitlxlnbK1sptKDvrOTeM5cxjHrEUabm25h6CjdrZhAMFs9M83ChpEUwMNNHBVMOd31skEVI0NG39K8i6DNjMfC18MKRExtHPFcgKWEKEsKDikMo8HIYHmKBkNpj5FVozxrtw23pu9ack2k1wRt6bmvS/SSoiMLHGqval4Ljkv/i3kNZesaWY9t6DaXkyVk3Y19SSesEXGhD0uHtq0nJeX2w2LwmCuKBqiIwminWjKSfZuvGumZDjMloU4jTetrF3XeqgzO13s2YpYhEGbq6/oIg12k6SyzuWYDHq5e7/9KmqXSq67nneFZdgN3DnBwJ3VXhy6UDJIRC2He/Q4zfox0puMirmqdc3Bwf2ke15A7qktbC+Uv8VClkEqVzWt7avuOEL3rmoB797p0nspD/FZeb6WYWWyZ++XaNswV2OfhU5FxOFvq7nPxEDPlbLm3ZfbhINq/anyMIemZaU6V7bZrGgRQ+7R47b0w3aSyW5gSnmS9yv/U6dn9xQtq6MEl1nncYrWd+2QapPS3T1H2bP3SwsQo/b6DZ35ZlWJQv/S2moJRecCdi8bKe+9V6hzISnBUiFptfW8msPH7OVkFclZO0TB6UzQNly9ci3Um3lSTRYnXNa3Z+rW+yvtvbRJ6vueLk1mhkYfWvJxq19+AXfes3Aqr2NSouftEzLRF7T9vhjykXCV7lcQ7H2EOlDbm1UQdO64Jc3qpm019+cLFf/UK57hCrVMQ6/scbZHG8FzDdhhOA38d/ptfZvaZffAKxBP2eHL9BCq0+wy66+RmHb+eobv8ogsp2GwLpu1wl7nZnGfO4FzqlqD73IQ+UCEyM9H6u6/+XAz97zSgYAD59wkpRitvGeqR8W5M0nJxWoJxE+V3WP/HTDt49TghH0sWTIBUhY6dym4RVtdd58hCKgVtpWpjtjkChYLM68HyxYvtWnv6YOIqaWE1YGaLC1WqGq15qdr08e5r+Oj+A3HJdSu0T/fKGgQTQ000MBlo7yr3zfYiSpdWtrdgmh1tOcpLm7867qD4L09D3L7yk8XJ1YX9vwZZ27/17bSRu4EHpy0moEqb4162DASqDlehb1uXhvs5FRXM+FehcM7g5jpBK7xEZzTY1g0k0k17WI/Wd+607TdNF62177/zpb1+GQrrKYgxBj11J7kbn7nN1j53t8kNDuDoOYYzlihf3qFF9nq8/7ihOl4T4m4WBe6pVQv7+r6BkelThACm10ZlGV34kplS7PxOoeXJpYVhmuZdV7u/VEOjydM8rdTPLF1kHUjpYx6sXfEiN+X13xZTJwaUAQH7+j5NEGllZ+tKU2abz1qIqcTrHrvWcKf1fLkSkX4UfF/+3bX+BmkTG0dC0Oo7dFUD8NjpbC1qLtakbn8ygWtq57d9TPO7fS5aNvx2eLve7sfJKTkXeTNDGryBwDIchZ/81jhxLZzzFeSK3U0zSbyOmlyOlGVfrgebPdTtvFkZzPlLQagpeU857f/SXHr0NDr+erWNsibRAFX2a2c7CrPxGifeJjFtmu/t+HgTbbf9b7BemiO1NZZErQwh3pLBOD2dz3J2l88Vl4zAJS5STaue4S+PZOc77aHNJZnJCodV/tZ+P21v6t6RFNVWGP5dfKZr6cW8MArrxGAaFSTaJ6URZ65RHtopFCh1VYKN63U5sqv7rdptv2FOWNP71Fmh79L36b6iSjUvFDu9mMhIhNttuNtKHMfMxbpZEzZJPKeCFrzG5e2vYE3F/G744QeCOHOlL7J3KB9AUT354kGGXTvZXhd5Nvd9mMhugfAoYo8+NAAG07bM8xmVmaI762vu6i2qvW9geogdk+M+B1L03Is4JOJKL8WjrB8QxjBtDJMFrNyVowxatvCyWLKoftKdc/15zDl6g82mCy8h4p+LH/ZwbvH6dk9hTNQIEgMEEwW8uZOeCoWDar6AfuxcbffvqfGuFlusbhccTwdKXKFxB6hs6jOhT1qezzDdfcZ6ojtOq8NWQuVgmkgSTnW/9xrZP1LI/4cc5MIao62QBjVZRFojkCO1R86Y9NFBGsBWKiTqbYc877aC0hgRTxkZAkxaGlAOkUPorC0OUROkexakQ1ct2gQTQ000MBl44ozieQnDTPrv0i885XawtZQ1FNKK5ZhoQaqV/4duZK4sbFUTQCz/h1E8xPvzH9I0vyLMxzpa0OJzbP+46fY8AuW9lT9y1hn7V51jtbNpbpWFm92djHoXw9Qf4CtOOhUexDf6YMEDat+o20BW8F6YTJKmXD67vYHWOZ34zuxD9+JfXXvwSP5+dDQ7zDg8uPa+BE6pubZm1tb+2aqKly7Ilml+j4L8/+BgYO4m7P4epLcdLwJV7bCq6FNZf7B+p49BXS4Bwk62ljXdKtt+7ynMvymxsF593vRE0EpE/lWYmGC507bSaSCR1PFQ69+B/aH5c7bvWlFXjTFvF1zqPYztZEWSprc5kdZ01TIblg6Zu26nzG19S/Q5RphHYJAR0fJ88kzchTn5LnqcuWHSAau5tor7eU1XTcaxJeSMYGgw5rohxztxDzOMj2PQh2fIt18fMmhHDd5Ze4KLC29cYFoqly1vlJDqDwsF0rvYzrgZawlUPLYrBMG55q+gKxYjcKoIZYPJit35hjsclScx1622Vlb/0vPHa25/cRN9zHoW1dzny8tc64lwL4FNMUqcaE5gIDI1pZ7iqFv6Xx86a6291h3YtaqeTUKb6jV1cOa4M3Fb6ryOEm0JqSGVN+DREDgwYcGWH82yLr5EABTiozWVEEQl39qFZ277tVtk121XUVv0YnfFV9Uj0dtU5n71BzZgSyGcmmeI7pfr6kDtOhxAZ3U5tRlZxNroBoeD6wd8ePJlmf0spfJbC6NGZEPRC7p/NmBLNnlVjtefzbITdEAQxctgnbbySZb2eQtSXLLc6htKrpHJ/hAkPjtceY+Ncfcp+aIvTu2oDeQ4TZI3JzALEvnqPaq5AZzGA6DuU/N4bvVV/f4AgoZV11Og8pO2xtaUfxbAGLvsiegWAiGt/SdxO+KM//JeaLvjJK8KUnH73agtWq0RfKJJ8zKhQHr25Wc+ey/+e9neP0rbP6V2p7KBWTlRcLXF/jWLzT7SZqlDGpFT6KyruSmHf/CyveeYyafodm17Z85e8t/qHvOoNLKrR3vL/5e6HsutyWc0+O0ikdxB9OEh0uJUxZaBJOyaXynDzK1438wsve3AWjfNIerKUdoyE5Abmq6vbQIcpnORa8s6+al5d2YeXJuV9u7eEfPLy39+EtMpHE1YEomyZuSNYnPBmqjQTQ1sCSYXH760wauDNfzszddl1mvshFREeoP7Hd3fdLmiZNx1J9QmmWhFuUDblNTjXAqJYGJiV5mwK8O7uAdPZ9etOqKV0NyF1bmKrxhCj/revtU7yhsWT5o4Gqqniwt7ups398/7bEm8IucJxN0Ixh6lc5L+fkKpIAuJzExEE1wYr0vRbo0PSEt7z2hiC6GfBuWdMxHHu/Dnyo948y6DIMTHns2G2Hpw9iJHi8r3jNatqUsxFECScoVyQhlxSvsuvnbtuPbtk5y4t4HSQdHChfP77FPHltv/r90dpUMziKBmm8g/dMOfrp2gJ+tHeBkZ0k4WtEWvhdR1BFFtWKbxuk7/h/ibVbGxGXLXyXS9wRm1/HKW8TjyacyFgycsxdRIna3/+UrXi7+LeUyOML2/YAt9GvgrnFWf/BsTW2s8uY2EfLxgSd7+NmaASTR+oblwndffIYVZB0Lf0xC2f/ykonl/PMVF57sC3ktL0FbmreKRw4s2KadVGcw1OUkmz97jM6BxVdwRVHHt/44wbu/Z/PCU8TSN/jBwd+ucZyKKGo0r5jH4asOPb2l473sbHtXzWuuPucrCrsvCYK1ytztWcFwYCtbmi1dKl00WRnYTpOz3V684pUdqbhWYfe6lh2sb91V9srszGTJS616LFq77gmWr3ipbpWj742i9pSeywlvqd8sJ5rUNpXIhyLMf3K+yoPJ8BnE7o0x//F5a5L/SWuiX7gBE7M4wU7cmSD8iTBqq/0bju+Jk9xeTf6akknkAxESe6tDVLLLsphy3jqoce+xe2Kkt6RtZIPu0Zn/8Pwle7q8FWAKJolbEqS2XL6I8H0vd7DjeDNCmbtbZc+zYryMnLnEGVbizgTJ3fZ2YCxyjti7YkQ+HCFwZ6BKLwosbyBb+TtjpDekCX8kTHZ1luzKaptDD1ntw7dncaLJyBNV5VyoIJqIio5TCVSVz6zIkNidqNneCzCx3lUltHaNzPoMjm4HptfkfHvhXeb762JfUHs8aO8+v+j9KLqJ6NCLi0fVZ6pv804GfZzuKIUEj7UE65aVsmn8x161zihnGNLba5aTxaXbWeV9lmwKOGM1xuWrhNWhnTQ7OmzbRIe+IBFXC0mXg2g+5DvgaMGvNC1yxNKR68qVPAwrUOgza/WdhsMgO1j6LqSgRK7X+o4yazJk1mdIb7hKmYbfBmgQTdcpDIeBFrI6T6lZWrILrh7QyQ5c/XQpidsSzH+qtnunKZkkbk5guK5MrDDXnSO95so+Xt2r21xu3wqI3xOv++yXAhMTtWPpLsuXhApWv9KVth5JUj6FfP/gb9Ut7+ydZW2rPaylHF4lRFN+sBtvKRk1x7pLIWPLlr9mO8bjDXP6jl8j1v0M7REHnow10dvUfEeRWFkaFhpQ63me2IL9iLe9hl+3VuNaNzzE6g+NVB2TqkOuGc4YqdCpmvsWgixnubDlTxFdtfsJucxFWhQkEq37OX3n55hd8V1rY96Ya/H01b6AWfrDK5cMremAB0Ew6OtJc1Pbzy25vn/916W/P3asi9sOtBe1tC4VfiWEr6usjzFNhLIwqt23fKOuoeTw52jfZmW6igw8Zt+ZP6a5d4quHdNI7hjDw6UJrpKyjOqCZlXGIZPLZxtLlGXhc9TQ+in3ltp+56PccuvXi78H9DacziSGkmZmpaVNVCJo8hMAGxGXD0OSUwi6jmvC7rE0N1fnnebh9c6z4cFTRWIpNGRNBrp3VXsY1nqKCxHF1TGbVTGcNjhEl31Hvu9pcXbjlmpPkAohDIsR93JsHtf4CI65+pnNynFH50fZ0XY/klD7/lbqXdxW8ATM48LOPwZg8ObqCZfPV+m1V3DnqT3G+vyz6O7qMWLbrV/mllu/xuBtF1jxgPWuFa/K0H1jiHLtc4mKjq8nwbrR/Le7gDfNdnU5HWqlaHfBy8/6v83Vx5aWu4rPpjAXEhchBwvvKHnn73H21n9Pc6LCpanANxXeaY1n09IyTnf3SVwV8+8HHxpgYMLyFMmsKXnkqXpv8e+JkJfJgHVv5Z4Y0Qei6AGd5K7Se9O6NUxHvkL5JtD5u53kenM1x+7Yu2Ok16WL43JuRY7Mhgy6R8eUTLJDWduqeW4obxvJoDVpqJ2qZZN9ct6yDx6cR/fpGG6jNLHKd5FGwED36sTuipFZl8H0mLZ7fqvDxKTvL/qYf3Ce7Mos6c1p5j41R3p9Gt2rV5F+9c6R3JEsLgQUxeOBX3hkgOZoqQ+vx2EbLgM9uIBtKhnFLKblqNSRvFTE74pb7SKgo3aoqAMqqe0lsi2ztrotxN5ptXfRLxK9P4oWLNkEekC32iYm2cEsmiCiiYLtvpe/6xwbP30SqSiyX7rv5J4k2VVZMhsyqO3Vzz69Lk16axojaODMiXXvXzBA1vN9Tb6xF3Td5ruXASWSpkmxEyILaTVlHAobP3WSvtstUeqFFZvsmAnYw4ODSkudktW4U13PO8PLgcXIxfo1KF+Ae9/Ab3Jz+wN1y1YOr63OnqoEDIvD/m42fuok/XcsFE5v0rtngs7tMxYptaQrVL//DtegzUO/HuLviNu8ClObU6itKtF3RJl/cJ65X5hj/sHqxYPEbQkSdyRQO1R0n077b7YTvydukfpSxTjUwKJYmr95A28IskNZ5HkZKSoReV8E02PS9NUmuv+wmwgRmr7cBDKo3Wpxhav5i80ggKAJ1upXfvVK+bJibc8JVR9qZkUGZVJBStSerJmiCaL1v6AKmA6T3DLLUjNlE6Gs41dbVWLvtgal7OosgYcCSGEJBMvlO7s6S3Y4i3JeQe3Pu4oedeF5zQMGmIqJoAsgUdRgSW9JE3g4gBSTwLBi4MWEiBSVEHICWodG7P684adC89ebyQxnSO1IFanTpq830fTRJtKSNalM3ZTC86IH54gT02GSHc7iPO5ETItkh7PIc9ZzFzQBw2kgZkUMp0FmTQbXMVcx3l5KVj+zQnnbu1yWRZ6VrXsAtKBG4g6LjBNOWO9KD+rF/ZnVGRznHOQGcmitGobbwPAauI65UHurB2NTNi2XZ9GKZU/uSOIYd6BMKAgZAa1Tw8REmVEIfyQMgO9pH8qYgpiz6qqFNKLvi+I85cS9z42YFBEQULoVTCn/XsqvKZhk1lnPQ9AFayJQkR1MCc9gSqVuxSMHSGr1V1U6mjowpHJy0brmyK2/jSFl0Z0x/BO7cMb78U9txyMHEMv48Tu7PgbAN87+d8p7/oTLgSCaOJ3VkzivJwJAsuUwvTMeemc8VWWWgoHlBxm/WBl2srTR5x25zcz1fIuLa75Ci/FpPjp3Cxf4UnF/aRINWh3DInzbXzHvSuA4bnkOOOYXmRTnjZCOztMk2w7iXOFDueBkdXAnh8PPFCd2Q/dZmlI530VEQWJ8658DEO94FVjME8lEaQ/nrwfv6vtVTvAiAP7hGbpXjdDUNEFKXQ1JWELcGE+sG0QwzlFm2zPk28COtvt5dPyfiOXmMOpo0Ogdpwl66mtOCCb0Dx6wbWttGatd1jbhzv9dWNnOW23DO+0Zwwpwz80ihicRtcshfEvXlRX78Xeq60lmmpngB1UkRKHfdzoknB6TnCuOLFvHj2/5czgaYoV/K2BpBsnxCH5/dVjiybt/Gd/Udjg1jNdnvVt/T5LwqRKJWCvktWu+IpxBMOm6aQZTKYhEVxxT8VM24CZ1Rf57N+qmRS4c5ju1H4DNd67Ad+7n+DIHqr+c/IasQ4RcfZNdAJTYPAN3XyDYn+DgP1ULsJ5tLd1/wUDv2TVL88YJ9GejlihsMXJCYNjo4kTZ8TmfZZCbQrXB7fXZyYkS2Zj3ZCqE2SlpenuPMLTsdUZ4GJ6wQjDW3/MKqJqNpHT4LGP65js7iHWfJrgsBmXNSRANundN4+tO4W7JcviLw2hpGU+rfSJaTtJt1geZy2aYLdsvOHLocskboUAwpZuO0+Y7WypXddcw4F2LwAgm4JDK+kDXPDuOBZlfWyJSgw7LA8DMdwymUN/7zJ2DVMW85I59bbyWDOOyZaAsFTqeX6y4/8CZqvMV7Kz+STfjbRn0GlkylS6FeFf9vie1o9qzJvLhiO13uQB0aleK3m29RBX7eFqwDyIfLB0rZAVbuF3kQ9Y+Nf/CM2szJX7WYSCoAnqLTvTdUULfCtlsw4I9KFwh4fFmIXF7bcHi1E0pUjfV9m4KfTOElJSKdpzWpFkk3Xj++6vwPH3Pc6UEHtuPN3FiIEH7vJOkWyO5LUl22PI+Q6H4fGP3xGjZ1AL54edXM2E2/Xedj/6OQvdsqe2vP1vtFQRWZkhJF5gPLj6mFGzAcggmDEx6GO1M1TVbJK+E5tWIvt9qc97nvUWPq8z6DGJc5GR6iJOdEIpGONVptZvCYs5ObQV+08WJ+Ks1zx97Z4ymrzRZHiKb0nhe9ti+i4/+tI+0Q+cbd1drDO060kz3nKXRo2P3gtGCnZCaKvY9a5t2c0YrjeuSquKcqj3OF/tYdx0ipKxP9clN9HiGORF7uWbRbs8KZlk4/Lx4WgSkK/D9CCpttL/zd2m9+GVms+NIC2gd3RI0aDICPFW27a7uT5DSYvxw7G+K2zZ/9hhqeqFFvVLD6dppaUg2D8c4/0TtzHiCCK1rIwB42tKMPNxf3Bdvfw3BkPHNbqq4goiZT7YiOXUcopPbOz/MVHqUJye/UXWNwjyFMs1xtUPF8Bikt6RJbymbb+RvLfpAFDEhEnjY+ta0VmssKcwz5QJVUv6dLMFxa/5j87j3N3SkGkTTdYLELYmiC6sypmB6rFYc/lhpgAh/onqwmP+F2p4ulWWVCwr+x/y41rhI7rEGiuYvNleRCdnBLIk7yozEsITeVOpw5z85T/AHQTKrM3CAIslUQJEAqkCBZALLyKlcRen7ZGkl3XSaRN+7RJdPxapTJcIfCePDR4rSoJXalSK1q/Q7vWlp3lPpLWn6PtZHhAgA0qxkEU+h0nORJ2W0zryhq1r1qofeP+llnuo61zJ6ygfduU9VTwL7PlKqlzqwsMFRy/0erOwo5RlSOulknnmElIDrmAtlSiG1NYXhMTACBqmbUsX7NT5vknQoeHP5jGyYOMsyUdX3HLDaXeu7nuNs+njFVlA9pZCddOgU8a4XifT9lHen/xRT0DjJC/aziWZx/DekDIP3jiEpJv7e0rkLGkhFYcZLdPEt3FEBfQPHkB21n7lr81fon692hTYFDUyJXqMF2a8QBbT2EUTNHmv+voF/zQketM4Vj3Nr088RzxNRkiCjmxqmy3qfkmEWXbAXQmFiXww1EU22NN/FkH8D4ewUF1InCCqtSE6LeBBVN84huzFm4xMEE++pA7RuLfU1y3r6gOOFC5JqKmXd6t9Vpl2gVLdF7+mDyP1RqOFU9ouPDHDvoRF+kue5hgNWiuH7ej7NucQRXpz5UdXdAmR3f4uhWg8jD8/EOTyb7N+iw7l4v5D1jbN6eDU+WSVDmMWsDkkQETWV0PIY8XEPemZpQ69A3SgAAETBwJ3XpzAFnaDhLiMXrAM3bP8ujluTFN8LkPNNACG2td7DCb5inUvN4nbbJ8aCbGCKGvGuF+FUmSCpYCeKHNLiq4uhoTgdW+YwKfRjlTdmJ8oCjiZW6QOcJP90K2zw4XU/5cixPaUqGQbezhRzy79PJnAWjm2qCstzuvLEc52wiMrfTcut56EIDlTT7hZzrKeVto1zBAcSYCWOpHmjtQou3fq/WDPXy6ln1yEsMnnIBs6j+FYgO63798oBoDSOCkB3j/UdmZJKj+dlBreXvsuhZa8X/1ZaTuHKqnhCC4SlFIitCpLX15OibUPpWxaKK7elth3veAV3uJS9Mtr9NOa0/YNt+fBDnOYhPN/79/nLaER6nmJq3ecpp+sqxcAlUWZX+7s5JfzYIppkF+ilcUkV7H1kwZPV0PP9eiGkVDBpXhVh/kSoWDblrB1+YuneLByqEXcq1Pq+WyIO7ny9nRN9cV7YcPkex5cCQVka2WM6Fx/b+v68j9TrKdJb08jTsrVACKS2p/A/WRI4jt0bQ+vSaPn80r0zrhcYTqNmONliKBBzVcg/fleu/uRb0UX27mtl2YRFPn/hfjvREPlghOYvNKP2qnh6PZgHTJK3JNn0HQ1dELj7lXYbSe8tGyve+VxJL62QGfKbd1xA1gUyTp2csnSbZu1ZPzcdb+bpTbOM9CSRNYEPPdHLif44+4cj6DVusSqsz1/qs/2pEBcrPiUnClv0IU4KZkn8ugLhj5f6nFrkqzsnseVEiBP9cVJl5M/AZGVyARPdYfXXkiDR7uoHanvXDPnW0yL2ovJk2VYTT3tmUbuwnNC+s+tjuGUfI/H9SE0xVrzrPMe/XW1xeOUAPodOzrlwlrago5X39v8Gj45/nrReGosLYfGV8CvNeCQ/U5lzdLqt6/Z6VzKbHa9ZHiDc91PENV8hCvDyGts+j1xNatYl3CrQsbliflJ4juXjTNmzdYas73Lw3jGS6k+YXfkkAKt+8gX7aQSh2P1ueNCyIc3HNAKKPUOvKZmoPSrxu6zn1ndTaU5Zb15aDsNn2Mj6mhBB7cyPMzWaieE0iN8ex33Ubc3jnSapnZcfqvtWQYNouk5QHiet9l39MCe1V2X+U/O0lc3i5n9hHmlOQm/Wqy3sPMpJpgKi77FIoL4/XzjMogqL6NfcKNBbq59JkWSCBUmmGwmmxyS9LU2a6ol34X5nfS2c6gjRlFx4ct62YY5cQiFaY3VOc5cPUDUaR35w0vKhIXNDP7btzgTOsukzx5k/1kwqcYz5oYcItVYP6D45xPBkjmWh5aR4pooEM6Q0We/EgvdRqUXjyEZqlhMDEwwE7OcSRZWT9/wyLWceoPXM+4j2PgNAtO9Jon1PFss1O+ziu6E1UxjDXyz+XmjiKgoSN7X+HAfnn7IZKjYUXIMEHUlQLM+3zlkYMXGXGRqmqOO7pZzAsp5XwcMp2XqIZVsHCW0ZK9ZrQ/MezvFYsfzM8LdqViEdOg2R7qIAsinmENUc9TKdAewbKLm/l7+7bs+KWsXrwN6+ZFVFytSflNuPLB2b81/AdeeFUsJ2wcTtrk+OrwxsJyqdo+nuh0lc9HD6hwN1y7rPncCztob3VA2cvOeX6Tz0K4UKcvO6l0i2nbcd53At7f5qob2l4nvNV6XTPcTNQ79ZJEMNx+LXECpDsPIGqNRukcqp5uMw7sc0LXJtOLjV8noVrb7Gr4RIUepnm1rH6Os/jHDidkxBx9uZKpJslcLQOd9FhodfIBDI+97UHIPMUh0r9jc7u5jKVK9M99yczwR4sPpsLS0XMNe0san/bqiWurJh9ftKxFGXZxmT7C/+9ocmaG4pTRwGb623Eg++Td9i+wLzpL69E5iCNQG+qfl+KEtkWP1+CttLmy5u+muc0cHi78n1/0Tz45Y2VKVXW8EBzbfpNFPrflKzPuXfzIbPHCJ28IXFzQShTon89pY1Efr2TCJdouh2Pbww3ANUe1Qo+UW6UuarGw/prda4rbVraO3Wt5UbymG8ZCCm857PXdZ2QzEQ1StT2xjK5fid+TB/0NrCjHxtph8Fsd7smqssIWGCmPdgHZiq7WVUQIFkqof5B0vEZOy+GFq39YwP9bVVe4KWoS1aTeh/6GdWuGfGobPfmeTU7gi6aBJKKCiayEwN3UeA1eese3DmPdzXjwRwaCIbRoIIpsCra6oXti8Xm3/lOMkpNwPnvJzrWtrEu9xBd9OZIJ3zTh7eNQUC3PNyOy5VQpQNXM1ZUtNuFE9p3tS8ZYz+6a2M5b2pK71nBVGg17OSs2Xb2jfN0b1rkY4aOHPHb7DqJ19AlA20pgsQX837B3+Ll/p/G9mtM7yqBbCSarhX/wjPxT5WOHpZF7yL7zpLtuuAb13ec720GKc4JLLecXojKzkVK5BLJhe3/GWxTKJtHznfOJyF+3s/AxQ8+quhuUrvcG7oBzgTfUyv+XJxW//tFzn/VFdxLJ5d9n38Wh0yrIKAu7jh/2I48yH0jjbAPi5t/pXjxMc9nPmRZesIooGv7N07A9b7Cg0lSNsIPzs8bWkCK+YYf75k/52855fpeOG3bJes53RxNaG2q8VvNb0ljTKuoHarKOMKSCVCK959aZkb3+poaDS9zaG31CeZrjYefHiAWw/eeKtiDdRH0mmtKoW9JfdQ2V1GuuVnGz27pxm6dxxPe5qm5gs4nbWNDUEQGH5gtO715gZ/jOa0G0DpoDWoN6+ZZ+ym/06y9VDd4wO6g5CZNwIr2v3Fjf+X87v+CFEpEYmCVLJ2vB0pmobtKyN9/hJx2yG4Ec3aXarTmURRrBWcaM8zdesH0OrqxRBLq7Adu0dJtpXCuyo/12ZnZ5Fs6XavYNC3js0td6J4rYG8c/sM63/hZMnYKkvfJAgCsyu+S/CeZ2ldZ3+upmRfCVa9U7S/2+5JFtoyWvw72b4fQym9V63rJJlQteYUQKT/cXp7D7Ms740R73oJv9JCr2elrZziVREdOps/e4zcxtJ7WUxjRwpFOHHvg1Xb/WsqV/ouofNboKggmKxZ+1Td/WtCu9jaficADn/9hYTBHRl2fvgoStkaUGVGu0pMbvi74t/l7URcRCS9b+9F5gcerrvf7RHoeedh27b2Dut9uiSPrY0CNkO/FirvQu6YwddTIqjmln+f9vazRQ+uyXX/xNmb/2Nx/8rbP89NO75rO0coNEF84DFmV3yH4QfOlfQhKp6ZKWp0dp0u38J74uts3muiWGpfbevn7emcF9ETqvesW1fuY3zLXwCgOeoTkZKnNBkUHCnbO1+36dGaIY31sFBVW9ZEyHmtEFt5gSQMUCKKKtuffWEAIrv+EQC35GN1cGdxu9w6ywr/VkRnbY+SkN+HPzBr2xbvKK3cB7sP2UKf+1otD45SdQwcjhTlH6bk0ortUF7iSvxi0CQJX0pi2biXzSeDZdpQ1z9kTcCzYOhLbYQ/Eia9Nm3zog5/InzFmZa2+axJ7BqzNvlhuA2cKxf2jjRcBtH7ohju2kRiZk1mQZJpxQUvq3506XbolpMhFL30nYdWRHGGsgzec4FlP7e4yHQ9FCauAJPBS9XIKcGVk9gVD/DJR/t58OEB3vtMN+98oXbGSMEAf9oaY/xp2Wrbp0PF/cvHL78e5TAFvWiveTvS3LGvDUkXuPVACxtOL0zWVda9I+xi9+FmfCmZnlnL3uy/4yIr3zeK5NJKntpAtPdpxrf+OWLQ6nPVwSco95gNOtpsobkAzatq989ynWlyz+4pLt78J6gui5xqky1dqJ5QaQHe0b2fNWufwrXrbxi95f+HKJXe9ZqNTWxtubv425CyTGzI26CO8rGnguDZ8pfMDtvHweX+TdRayS+QStnAeWaHv1sci8rv2dNmLZdlfWPMrfgey++vvZDRvtFO5MS7XkQNWuNtvWyn/p4UDtGFLDjo2T3F8nfaz/3hod+tOkbPS2kYYhZNiTP4rhHaNoQRK/oetfUsslCb5HdlRRyqiDsjoajW83CoIu7s0ikPwYDmqII7U+o/hVvi/P4XQ2w4Y9Ul9q4Y6a1pYu+OLclr6u2KhkfTDQbnCSfZVVdf7PtKIGsCP/dUJwf0NBHVivdP7EkgxSS0Zg3fvMLPnbEm5CvGfTy7dg4xLaJMKbgOu8iuyBL1RGmab0Jr0vC+7CX8sTDeZ72ktlm6S4GHAkQfiOI84cR9zA0mtVO35vsiaVYqeR6VRU8EfxQEA8SkaIlBNut4XvMQfaA0yCjnlGIYmvOksyorh5AVUCYUcj05xKyI+6AbUzRtYXnlcJ50WtfLxwabuok8L2M6TQy/gZARCD4UxBRMxLSI4TGIvit6WZ5Rvsd9iDkRZbJ0cGprivSmNI6zDoSccEXtx3naiXJBIbkziekyUSUZMAkOxYme9ePrStkGfDmQxuMoPZeV7xsFRhe8hrezwjuqbHyZXWn3kEm07gdz6Ya0iVmS15Ht7ysTsOolKQaGap3TGSxNkobfW+3RkGoprUbdr25iBLGmWtCOnSXDQHPNc377H9eto7zqGBf799fd7x+MET5fmiRubrsNwVD45tn/UfQq0AYOsG7raU5+b5DObfbJ3PLl+UwnSgYBmF9mpb5tWRNBHKqtNVCA0ryw6/fY9tLKmrpAtieAoWX7bL/7epqBEhHk606y4t3nmT7YDEDHljmio5ZxagqqJaQ+s63muZ0DtV3mW3adtv3uv/csgcGF72kp8CoBPN76REKq6TiCUX+4dTVnMA2B0Jaz6IAithblc4KzNdxlasJuiGU2/Iiu0fV1S7esiTJDucaB/fhVnzxqa8u9TTM0NZV0wOaH7OGK6z55msP/PIxWFuohInJb54c5lHycjl124XrnqtOsWGWv06rVzxX/jne9WFVnV4V3VjA4Qzj4LVxRy8h3+POrjU0ngR2Emup4KAoGQrDU1lpWPMOmTKnP7N41g6+71D+0u/oZDmzj2anv5G/MZMU7S5NLS0ei/iQ8HTzD+Z3/uex36VkIuoIplUg6/21fxM+1Q86f984pa49BpQ2TilXYOtncCuEpBRheawIiCiKbmm/nBF8AwBcS2Za5h2N99klRAfI9X6Ti9ZNoe72optwy9BItZVEoLT//U969/7MI2nLCvEis5zl2VkiBbPjF2skR3A4Xrf0mY6cvb+z7wJMlkXDOK4yoOZR+q56d8y5Wfa+V1O4kOdnAnZUY7baPLc1fb6Lp1hwjPUmEpIhj1Oq/M6syl+cBbVqECcCZ3iSbTgVxaCIvrwnbCPF7X26nPeKqCt8qR9+UG9EQmA/kiHtLE9xaIR8FmYLQt0IYHsPSzkxJyB0yyR1JMusyNH21CVMyyazN4Dzj5CbdS1wzOJfMsWPKaudmhRB9rjeH6TBJ7E3QTjvZp7IggDwjI8VL47vu0cm8N8Y7Xm3nuT1hknMCpmLWJJacOZH2sJOLoTqa/gABAABJREFUrWl0ETYcDxIPqtx6sBVEmP6qJc6d2poqem2VP1/A9izXjlpfpSgb+HsTDN61kOhxCe3zTqabskta0zCWmj3zEtB1wc1Ej2VTdT0SYpfbw092lpI3rB0NMN5qt7ncZaGBrrMOsp0qpru6f7v7ldqZ0gqYXvVVIv2P27Z98tGSLs/a0QDfuOtCzWfTWsN7a+WYn5Vjpd6xQJJIsoFh1H92ie4X+PDM1mIg3fLApiqmuJ4+uO6b4O5Nz1DewkxBx53Xrku076fp/D0IqkVclS/2ADjyQkC6I86GDaVnMbnh7wlOlDIqj+76Q0zJOqcgL50kNzHZ2HwHx71fwUwbxQVF1VkiiXV3fe802aWx6TPHGBV/f8HrOBfQAuv1rmSch2rue9/AvyYtzHN+3b9Z8PwFnL7rX8HpNZzf+V/I+scKMkqseI+9DxMRuWPbbh7f/zMMTSy+z4FJD3fsa8PAREQgo+h8/e4LfOBnPTg0kX9+xzk+8ngvzryNn5V1nJrEoWVR9g9HMATom3YzOOEthb/+3DkETHYfDTLWGuSjT2qc6YmTcpUtcuimJdkpgKSDJpf2zaZnaXXbQ/3eTmgQTdcJCoSGNCcRfCiI2q7iuOigow1W+5xcEHOcPmcgaAI7TzaxTHLyraZZpHmJzOoM7iNutGYN58Yc8wGVDa+HMEI6Z+Mqpiag9qiICZF0RxrJf+krXO96tpPWmJMv3H+OQEJm8wstPHvyDK6t7Wy7EKTF6+BOHGimiaTA6M88nEzkkASBu9vsqyMtX7avJvleVxg/PMqmzX20OCQuaCrNX2ojZZi4TpVWHVZ+tZ3dzW7OGjlSusnZcSezPVmavtyEqIqoXSrvN6wg8UOxDHIuyZrWFl6PZlgfcJLFwC9J/HA2gUcSWHeiiWMJi0io1B9Y6XUwkdGI6wa+53ycO3mcZatWo5cNRh5JwCUKzKsGHQ6J8HE3ufx+tVPF8Bo4zzhxiQIG0DbqJmYYHHr6EHu3byWm5TU5JIE7W70cT2QJY9BnyJz4moJXEolrBoookNAM3CGDuGHSlFJQDRPNNHll3342b9684LsL7ffSvN9LJp/RxPe8D71DJbBRI+3VSZ6UkMMyQlYgtSWF2q+iXFQIPJqf1GOi9qooF5SSwPBZJ+/u8GEK0LwqQv/tE4w900HfHnv2qWUfqu9dVAurf3lxraFyFMK4loKcb4Kg34uWd/dNtRwF1uCVQyxfGULPaxMVFrC8cpCuXfXDVCqhuuYwlCXqfjWfqLvPsfVVFgpE6r/3LP1lv8/s/S2C43vhrBX+kvWOo+cFrV1lbvOVoS26Zx6pzGPA3ZJl0RifxXBZulcWHPf9gHLKx91iGV2FlTRPewbFq6JnJebe9YcAOGN9dL3072znGbpvDM8SyaPA4NJXoVZ/uFoUuIDFPKzGbvoTW2l3axp/b5LkpIfhB+pPAo07vs3aunsXhqmkWDG8MHFYjs2fPb7g/qENjxT/zgRHiHU/X1Vm1fvPceSry3EEcmhpCR+ttLv7WXbLBXJerar81UImWO05t2fvl2uUzJdvO45cFlaQ7HqFyjx1gf7St9G6YxRP2AX57s3pz9mIKEEQOHX75+per5xksn7/1+Lf5STTGwlDSXHi3gfxzmxko/YOZm+u8FZcFsPdmiE6ujTaS3XPMjP8zeLvcN8TqK4IoucSMsnWS9mVh9l6nEhvbc/ByglteV+05R4Hqd6XaLkL0nNOTnx72dLrVIHNOS+zM9AWlSDvlHGz0wuvleyc5HGNmVCW6aYsF9rSbHO46TkYYM/BVk4ksngkkT63QuSMziuRDOE1KdLr0zjOOZCyIt4BnVwOQoaMZAiMdCfZdiLEyb4Eii4Qiju49ZA1cSn8D9bEPScbHF4WQxcN2iMl+8mTltBFk6yz9IydOZG7XrOTBd+/9SLhwMJtslzLxHnCSdeqLjL5QOJyXdHMhgy9D4Ws5+Yfx5//fjKrs5infAiGQK47R/wei7y896V2OsIufrZlBldO5PRtEZq/1Ay6pWGZWZdhxZiX1qiTBw508qX7zhNIytSiD+94vY3OBcLQ+gQHobTI6Z84SAZUBENAcZoYQYPWXpObj7XwcCSO5tZpC0hFb6b2zXNVizcL4f4XLW+P2WCW1qiTr909xsrzPlTZJOXSaI06mffnONalLOo5eTm472C7PbQ3BR/8Wa+tTMFDqBzbX2rm/DNT3L92AI7BM2aceBy2BVzEnRovrp2nd6b6uMK4DTW+ycqyOYn3PNvFD/aUFgS8L3jxLZFpL0TaK7KDvWv2MsHp+mWFUpvOBkZxRe0h9/U8IHVXDN1ltxNMUS0SU9Orv0LOM0Gu3+qX0k12slv21Cd5yr2uVe8kcsaav7ge+BbKV1agJpQFCcpI7xNMrf0iLcc/iHP105zkafjBp5GcOplQfXulHE3DMRZxfi6i3AN5YZT63qx3nNlVX6tZqhCNUAtZv932LpCKBRS8ugZafJx9tI93vNRh+97F/INzqRIPPlySKvjFR+yyBU7Nmg9vGAmyYSRYsy47jjVx88lZmiMCZ9sh5VTYcQLOtntIO3V00eSXHg6gyipJ7zx3HzD50O+V6BXnEjQs38oQzAXSPN4I2L59u/nqq5c2Ob0e8d7772BnwM18xiCqGqzyOcgZJg5x4YHnUCzLeEblHe210zkDzOd0TiZziAK8cOAQn71zG0eWxwjMODiipFAGdSQBmqMOkm6dO/a1cdGbIZhwMCJm2GCWRPd+tmWG1S+F6HJYy3AZ3cAlXbsIzIsZjZims9q38IeqGSbyIs+qHl4Kp9nZZA2Y59Iqkxmt+LsSWcPEKQqMplQGPfWXIhd6LrOJFK0+65meSeYIKRItjstL136pyOoGzop6jWdUjsVz7GxyY5gmL50/z0BnN6cSKiLQ51YYcMuMZTTimsHWoItzaZVhr7Uq275llu4dV0hQvIGQ0y3F8I/DXxxmb9+7mb/jT4v7j35tObmYg/t33G2Ljb/eMfZkL323W94K4sRyjK4zjD3dSd9eywvF+d3f5mcd59l9S3WmjusN53/WheQ06NltJy7PPNSHM5ij95bS9uDY7bQe/Thn7vvMG13NItTZFSit9Y2mcuTictHzZmp/S7WIJiDF29H901XbbwTExz34e1KkZ51M/mAHdy1/PyO3/dabXa2rghPfHkJy6ax4lz1UZuVPPs/Jez/1JtXqyiEl2tB9N04fvhQYuoCYFzR3JLryAvgWkl/7RJkGCrSsCdO1Y4bD/7yy6jxvBE4mcpxK5nCIAmv9DnpcV1f3aaopQ0e4NAn73p6LKLpAc8zB7sPVYWTH++O8ujqMJptIOkVhaNGwHN2WkCwUsLzdP/ETa1nEk4uz4XyM0dYgB1aEeWRHifAq6PFUTgJ10eT1lRGODcbonnGTdulsP9ZU1DG62JKme87NwzsnmWnKYoiw/IIXTVK5Y18X1wJrP36qmMGxHPv/dg3BoRiZsJNs5O03sQwMxMnFFVZ/8GzdMvv/dk3N7efTKlNKjlQMlrsd9Lqt9i86dIwFhNdXf+gMriZrsbhp9D7Cg4/WLdtx5FNMrft88Xf3/s9xcfNfA3Ds77ex5jO1BbfrQYv6kINX7g1dDlFzYcglQmV6XyutG+YR5WoCXk3KKLUWbx7/ebRbv1dMLvFmIDbmJdBXIqWknA/dsfRnVW63LoZczMnRr13+wsFS0RFNklZkYp6Fv+09x8+zf6CDJzYnuelEM6+uCvOVP3waUXpj5njXEoIgvGaa5vZLPq5BNF0f+N8fftebXYUGGrgsWCKKN+akODnlwtthXykh6+biix0M7Eigupe+cnk9QYi0YYbsE0fluQ+g3vLtN6lGl4aT/zKApy1jI5QAUjMuwqcC9Oy2t7f24x9jevVX38gqVsEwRMRFPDLATjS91TH35fexbnMLk+v/4c2uylVBdqIFZ9fS9ZIauD6x6idf4Pnp7zOWtLz4Nn/W8mbd//erIR+CI8oG/XdeZPKVNjLhtx9xABD1qART1qT/wPIom84EmQ5l+elNU3jSMlG/iilAS9SBrAnEPZqVGcyELaeCbCrT/alEeTjfA093EUwqiEtlsCqgiyaSIbB/RcSmNXQtUGgrlTjwj6vY9EuWp3I9QuVKIcoGfbdNMP5CO1rq+hKhr/dcynHkSytQl1jvpuEoA3de5Pi3hsjU8Uxb9YGRvCf2Es539ucID5U0CVtP/Tyzw1YodPDsPUSHHqt36A0FR6yPXGDpnvhvBez/29Vcz1mmPvPXnyfQWiOd8g2GyyWaGqFzDTTQwBUhMHDjZlioIpkAnGm6bxvlzQlouTqoJJmAG4ZkAhBF06aPVYCnLVNTdHpmRW0dmDcSSyGZgLcNyQSwfE8CYm8dbYIGyfTWwIl7H6QF0A82M3csVMx06G7O4OtJgSFgmhAaimPqAuce71n0nG9FBMtIgU1nrLCS9oiTjz3WX++QJaNzzsk7XqotInypkPLk4LUmmdZ98mTdfQWSCaBz+zRTr7diGlfH29/dmmbVz48SPesjOJTAGczhactw6vsDJCc9i5+gAoJo0LF1lun9rZa+TR5rPnqa9KyL0cd6kT0qikfD151iZtEkPktzWFj9kTMc+qfVSC4NAWx6fuXw9yWK3swtqyOMP1+7nSyVZAJsJBNQJJmAtwzJBLztSCaAwECC2LlrqWx4ZXD56kccvR3QIJquA9zoXmU3GnKGyWhKZaXPCv1K6wbuywj/2xfNsCVYXwOgHKphoogCWd3g668d5hd3bKxb9kQixyrfwhmBrif4ui5Bg6OBBpaAzu2zNg2ccijeai0FU65BGDbwpiMy8NYx4Bt466F947wtm9Kqnx+tKiMqb14IylsZV4tkeiOheJYm0ty5bY7QsjiibHLuiW6Skx4kh46vJ0n07MLZ1iyU1Mglh15sl8EhK/yooFcz/MA5Zg430bo2zLMPyzz/eifLvM6irIFVNo1pCKTnSrZqy5oIndvmaF0XzoeL5rU3AyrOgFoktgrIxZS8Vlttr5GWNZEl3BNIiknz6jD9t1lhUQWZgkqUZz5r2xDmtWdDdIpW/QXRoGlFjPmTtfV0Gnj7oXVtmK7tM5z43lDRI7Uc+6IZ2hwSIymVlG6gmyaKKPKheJztqTRf8fvQt0NfrJkjc9qiUi2XCoerthTL2wUNouk6gCAIzGQ12pzW64iqevGDSGgG9+X1lyazGq9EMryz3YuYFw1MagZe2U6SnEzkiiTKtYRmGLwcyTKnWoOvCNzT5mU+p3MmlUM3LcFsA9gRKn1oExmNA7EMqgluUUAU4MChw9y5bRM+SeRoIsfeZjfn0ioXMhq6aQ1vd7ZaAuNjGQ2vJOAQBdyiyGxOZ63fQUQ12BCwOohXIxkeeflVfnHPTuZyevEY3aQoiq2bJjHNYDpXbTw4RQFFgERe/Xv/fkt02yUKuCUBzYS4ZnAhU4o7DsgiA26FQ/EsHklgldfBxYxGxjCJaiVj9Xw4xg+nrOMKdRIAryyS0Awyhsl0TmNPs7VSdTiWJaYZtDokxjMqIUUipIgEZYmXz4zyjjXLAUvvKWuYrPU7eXQmSc6oT2C+u6PEsB9PZAmrBjdX6FKldYPjiRwtDol+t8LxRJZTSZVmRaTTKXM0keO3UzeuN1MD1y/qkUwNNNBAA28o8sPoWMagz2XZWq9E0ggIbA9Zk99HpxNFOw0gJyj87VPPc/+2TVzMakgI7G62xteHphI0OaTieHsikWM6qxHTDDqcEluCLqRrIArdAByOZ+lxyoxlVKayljbpMo/Dprc5ldXocJamRjNZjUPxLHe2emudsi4K2kHDD5zj0D8PM3DHRQL9SY5+1UUuvrB93nnTDJ1b55g9Glo0bLNtvSU2veedGnveeYETx93Mh2Uujrg5M+rkdz5rkTbl4XyCaDVq2WXgbsnaSCiwFnrKMXTfOLmEzNGvDBe3vR7NsDW/0OrtWPpiY4FkAlj70TNLCjNcc/9FvvudFhTF4Nd+3TpeV6+dNmwDNxYKiTuCWyf54aNBMobJvgMH+fgt2zkSz5Iz4ULG8iRfvz7J+94/zzf+dytCRMChG7j9OnvfH2ZkJM7Il9oYmI/j7r86nth3Hhm9Kue5kdEgmq4TvBzJ0h3ycCEyb9v+/5+cJLbBwP2CxH/saAUknDMTbI2ZfNvnY78gkc+oyFB+sDyTUskYBrM5nZRuoggC97V7SekGPzxwnA9vtfIY5QyTn82l2N3kxl9BVs1kNV6PZRGAeyuyxr0ayTCv6rz0+j5bxjMDeHTGnpUgmo8SeXg6gWaCJGDL3JbOkyGJnMr5dCmk5Ol5+8BlmiaPz5Ymn0ndJKmbhPMJuPfHsjgE2BBwcjGjMpHVME1re/kx5Tidqh8clTXMmplMMoZZJKoqEdMMDsWto1K6yb7Y4m695XVKlxFeEdUoklEFFAi9hK5xIe/Asf/8BJ72LlyiwFT++DML3FcBD00lGPQonEuraPkq/GgqgQAcPHiQDRs3FtfUxjMaMdXgXNo677xqMK9aRlSHlKZ+UvcGGmiggQYauHERHEyw+bPH+Iev/xofmvsJY2mNyaw11r4SSRPXDNZtSuBMKyTnHfzD0C+jig4ij/yMo4lS+O9ERuOE4WVq6Ga0s4/xs9kUXllgKlsa9yeyOqn5NHtbPOQMk+OJLG5JtHmoLIYbzSP6WkDPqWyZDHPR62K2JcBL8aN89t8JPPdXncQE+NCDs5wfc/LjHzVxIpvmZDbNbr+Pc2mVkZRKUBZZ6XXwejTD0hPN18fytRN48pPhTeOTRJNeTna1oOkG41md+yIxjne3sOkzx4skEEDr2sglX2vV6rztfLN9EdDVlOWHJzVyhsnH7ytZbas+cJbIiJ+546HituBAtXCzw6fhum2UJ37UiqRJzGQ1tgZddPn7aV61uD7TQtBNk5/OpOh2ybhEgc0V+wcGsvzWv7lo2zZ07/gVXbOBtx6GtkWI/tgKoZOdBofTaVTTPrd93/utOfaH/61Fps6cFWjLq0p0B1T+1/SMJZuRSvHM6n4uJrLsODbCzOZhdIeblcGbOBZ5EZOS88CW0Un2Ddo9NJdNh1k9YZ/Pv11x3RFNgiC8A/gLQAL+wTTNP1nkkLcEZvv2MNKzk6ZzTxGcsFJSuwyD3LBJ8r06yffq/CYXeeSREOveEUV6WsTxsAPcbkysRbdyguFcGWmTM02emLU8XE7OzKOZJqph8tM8cfPknPW/XxZJaQae/P8BXSchlj7S82kVzTCZyF66xoiKCIKAbl6NYbs2ciZV5MyNBZPLEbSLasaiZE9PT5ZYTCIetz55nWpCqtCONMOwRdybwNl0NXnl8+lE/2DpMfINNNBAAw00cCPiv247xJGZCUafCNLTmyMakZlMwLp1Kd71njAQZvbvP4Yq2kmeYV8/c9oskbY5jgQ/jaF4abrwHAk1RaKGORTVDJ6eS9m8oMM5nR15D6iXwmnkQCfbpOpRv2D/nEzmbF7Lk1mNTqfd3H92ZAxXoJntFZ46HTdNsX/K4NVX/Hwg4CDrdtARTfK8JLPc52QkkeU94RiH+6rFbUXDwBBFNp6f4mhPK1pFpqVb2t/HiegrzGYv1HjC9dG5fQYw8XWnmD8RYv5EkHq2Ulc4zhp5EkUXMCfTfF+U+Ny/t8p+7tdK3jStbRqBgM6yZRlEEf7zH/UV97W5ejiViqBTCsl2BKo1AwEEXcGUFl7c89xUsktD6Rxt0yqdcjMvN51nzbiD3tYkF5weG8l0tbH6QyN8K3+Pg4N2uy20LE5o2eLe6atXp1m9eozZGZnvfMPSber4WP1Mb0vB5s8eKz77i3qWj33krZX9soE3Fr//B4Wwy2ZgnLExB1/+UhuaVtsDTh0y2bEyRhzw6QJNN/9r1AuvoJ1/jvsPnLEKORxw9Bzj730P3q0vMfDYKvToPLrHRzgzQVdCpTXuYubf7QMg9G9ceDIGkY9pOI81PO+uK6JJEAQJ+GvgHuAC8IogCD8wTfPom1uzaw/R2cRWInTdniH1SpaJZBe//b6DzLXaB553vCMCQGqvwX17Z7mPigGyLcfMjIP29hytrRpHj7oBoeg5I0nwih5hfq7kKizLBl0eld+4ECH2v3KcOuXi619r47/NzHJaUfhnSeDmNh9r1k3ysxEvJBy0tZUGXUkyCQY15ucVhoYyhEIaXq/ByZMupqctoyt1y4f59R1f4gc/aCZgTOMPaBzY78Pv11DzLrAtLSrhsIxRI8aWGq7khqiguptxJkuZqTweHUEAl8vgP/6+ny1bxvj+95t54IF5/uovuwiHr32Tb2pS+bVfn+QbX2/l5Mmlxeb++m9YqzWhkGV5/vmfdWEYAvfeF+HppwLMzSk0N6tEozK6bj0LRTFYvkKiqztLeF4hk7Geo9Np8Du/a632HD3iZu26kndYPC7y53925cKmsmxUrTA10EADDTTQwFsRweGn2D0Mu3eXJuTHH/ew+q6Sp/UtA/cCMQIIPPurP+Tvzj3Crwy8g6lVXyYycILU/n08P33rotrJUc0g62kn0b6B5tHHmcrpvBBOc+T4CTqWrWB0xcf4Q00ko8U4EnmevZ0f4PTMISYy+3E7vJyOj/F6JMrWkBV2FdcMOssisB5xrOaVU89y68aSzo0kyDQ7u+jaeowuYG5/L3fPGfj2/A7Jp/4Yj5nldfcy1nqcDMVH6D50jJw3RNLt4XUdVqZmGUhnMQUByTTpjiR4ZKMV1m+YJr200NbhpsX1Xn5w/q8AcIayrPnwCMe/08G6V7KMvU+he+cMJ787SHAoTsuaMLLLrpHl60rTf/uEbduJbw+h50S2ZS4g7FGZv8U6RkLlc5yv+5xXrCgRSU1NGuvWJwnPK/ye/1dIaCn+6/R/4sEHp0klRDwjtdOTL0YyVSL2Ph21z8AIHWc1IM2mmW2FFYxe0nnkTAjNFbmkY37/D8bY95O1l3RMLbS2aXz216aAqUXLLgW//wdj/OAHTQwNZenrq03oNdDA5aCvL8fv/YeFvd/iD1jzLq1D58wn/gdDz/wPpOYhRG87or8b0WXpqrl2/SGpwDmGum6jveU9pINn0JUVjP/Ka8C+4vkif5ohkv87dWtD40+4noSoBUG4GfhPpmnel//9ewCmaf5xvWO2b99uvvrqq29QDa8dHn9i+RWf4+T5Flb2186K86UvthGPS/w/n7NWdKJjDp54zsf7PlLftS/0BQm9BWZXQijlJL2lWjfl7Fkng4NZBAH+6i87i/HTBTz0UAiA+++PLFj3dNrE7S6RSePjDn72RJB0WmRy0iKrRNHE69XZdXOcF1/wM3zvILi9vPaVk+zcEeO++xa+BsDsrEwwqPPaa16eeDyE220gyybRqIRpCkiSiSia6LqAYQg4HIYVgrf/IJ/91SEO7PcWvYIqIUkmgmCydVuS++6LcPSIm4ceamLVqjSrVqdZuTLDY48F2bs3gtNp3evXvtrKuXNO/v3vLdwR/un/7ubf/NvFiZ2vfLmNj39i6StCyuQ2fnxgEFn+KXfeFeUf/qGdx396jLVrN+N262SzIi6Xwa5dcXI5kQsXHHzyFxorTg3cmGgafQfhwUfe7Go00EADFRA1F8ZbQNR/9id/xw5XjJG9/47OQ59BUn3MD/2IdNOpYpk/+Mu9KOGzVce6RAeCIHBzy0a+0L8LQ3bR98pfIunWczl99CQvfvob/N/MDL/qasMUNEzBQDSqQ+U00+B+c55b5l7gxOQov9E5xKam2/mIqPF9MQTA7736P/mkEUHd3k/7BpGO45/g1F3/CoBVP/kCengUqWkQQ8og6pYejyFlEXQFtBym00TQXQgImLpK6sn/gpEOg2bVNxJSeH6wD7eeY9W/GinWTbnQRNqXwEi04OidxPW6QGbr9TMXCV7YS9Z3kUzo9KJlfVNbSXS8fs3rpKTaUD2W7RW8sJfOo5/GEHP8Pf+e2++e52tfS7FtWzMrV169b+jFF7LsuvnqCiM30MCNgMD4Lbiiy3HF+3DFlmEKOud2/hE5/wX6Xv4PaK45Jjb+7ZLOtXfPqyhK0zWu8bWHIAivmaa5/ZKPu86Ipg8A7zBN85fzvz8J7DRN89fqHfNWIZpue+KrZGl06DcCUikRUbS8pgBME3I5EafzrcRcCyw1Ze1CEDUPhtwQdm7g+oCcC6I5GqpitSAaDgzx2q0mX+vzN3B1IWledDm5eMGrhLdT+0gmeojFyxZsBAFRdDDgasGQM+iOGJPpFjyuOdxpBROTWMYgKPuQmmYQDBk500zOa3n2yJlm5FwA3REHTARDQdSdYIqowAg6w0gUgjhMUcOQMgiGUjwHgIBY1B5xxQcwxFxxvyPVAQjkPJOAgJJuRXVb9yBqbhzpdlT3DHI2RNZbWhQTTAlTuHaSCW82nMkeNCWO7ohd0+so6TZU9yyCIeFM9Ba3x9QkES1CIpnG4/bg95n4hACmr/ai81Igam7kVBvjyXHaOiCdNXG73xz7VlK9mKJOOA1eowm3kEP12+9NzjahOcNvSv0aaGAx/MuuO+n0vH2JpusqdI7aQddVs11BEH4F+BWA/v7+a12naw7TMGnWMmQaE/IbAk2eig0CNDjC2pB1E02OXLPzi5obQ156xpNKOBM96EoCzbk4+eBIdpLzTi5a7kohGAqmuIA7vimAcP0sELzRcMb7yPrHFi8IVc/KoTnIORoGaS2IhgdDvHZjkHd+PcnWw9fs/A1cXSiqjCq/cd+KaLgxxPp9uSPZZSNFbmQ0+cIkUh2IepCEOUdEbGZAcBPSU2S9oxhSloA/7+WcVzloDYBnfjUpYdZSMPVOUlRXcoUR5dpjoRvYqrlxxQZQ3XPI2RDpQMm7qm4uNX/Yvt9T8dtd5g0vh8F/ETeAPI7NRLruEugJOOO9Sx9DFoEvNQA0kWg7d1XOVw9O3YsyU5jvlMa0Njwgeoj4DUKIFGWl0j40Z4RMYBR3ZAXpJXhnFeCN9CGYAh1yL8yBamoIuRSCKZEOnVr8BAtAzjQDJkqmFc0RRfVM1y3rSHbiSFnZv+bQGEIGHGimTiZgeQRKuQBKBjJXgWjyhFejy8mr1jYauHqQMy1oroXJU0F3IhoyIKAr149er4PKSePbC9cb0XQB6Cv73QtUxQuZpvl3wN+B5dH0xlTtGkKAv3ximJnhbzI/9BAAcroFzT1Hy5kHcMb6UTKtnLv5D4uHuKJDZILVrtcNNAAQHLudaN+T1+z8nvk1uOdXkQmOkGw7WLdcYPYWYj3PXbN6+Gd2Eu966bKP7z3xb1Hds0yt/edFy3ae/SUm1//jZV9rqZCzzWju+iGt/sldxLtevOb1uNoIXriNaO9Tl3TM0LN/jCPVxYl7HwTAFVnOwMu/z4l760ZT29B+/BNMr/ly8XfLxfcxt+J7l1SHNxKFfv/NgCuxYkmhIpeLVa9/wfbefFPbSHS8ds2u91aFoLkwrzDEzD+xA//UTVzc/NfV5zdkTFGjefL+oj3yRsCR7iXnry8SvfK5f+LkEr/7FU/8FWdv+Q/ozkv3MgmM30qs59lLPu6S0X7ph7hZTZrjtXcuZM3LWNq41kluOKz8yecRKhireNtrXNzylwse55veQqLd0k7pPPxLBC/uKe5LtqhMrP979CUsMpWje99vcHHL/4H/j72rDLDjKtvPmbku6xrZbLySurtRB4oW6ffhlLZAoYUaUtpCP6AtFCmUQhWot9Qt3rgnG88m6+579/q9M3O+H+N2ZXeTpvQ+P5K9M8fGznnP8xqA4o4LULNnPgAocxuT9kFwqoT9vEWPYmDeCxiZsdC2zVy+6crOL6Gs/dK8xipqQMXxDc7ejaHZrylnKvZ/FuUtn0Ay0IHWM3+uqzVvy+MgMAYxFhnPtlNeRKJ0fOvE/IVPGo6UAihF0t+N1rN+AgAoa7kC4eqN8IRmY8qO6wBoyVNZAefCUP1+DM57CbPWPohUYA86T/r9uMakG9/GJxEta0bnyQ9MuK0PGyZrPa5svBoD814ABBZgJs+Scf6KJ7Hvkh+bjjsSJajc9yUU9Z2mO04Jh8aLv6U7Nm/h42i85BvK77p1d8I7NkuRLycbhHNj+uZbUHzKYce2H1IcbkTTRgBzCSEzAXQB+CKAL3+wQzr4IIQgvP1ZVOJLKG29HI60mJ6Rc4aVvwFxkk67h8FyXkBwAAyHsdq16Dvqn0qZqVtugnd0NpKBHnSceq9lfxX7P4vBuS/rjpW2XoZo5TZLiw2tb3g+KD9g3tTNXPlbdB3/ZzCCE85YtbJhLmm/CJ7QLPgHjwWbDqDxkq/btlvZeDVCU1YhFehWhGI7yAsb74yAEh4Cm0SkehOKOy5A/xFPY2zqKsxe/gdw7lG0nXFX3teYK7wj8xAvbQTh3PAPLUC0ogHBvtNAmTTCNWKWwZL2i1Bx4NOKRcuBC7+nXO/AvBcAAI5EKerX3AtKeDSfe7NlIMqK/Z9BecsnUb3nKxCcMTBpP3hnGI50EdpPuRfx0v0oa74Sw7PeyjpuwrvgHZ2LeMk++AePQ6RyG+Yt/ocihHSeIC7u1bu+jr6jnzDXt4gdMZkg1DpAZ86gBAPbdwK5xMekhyZ7hFnAM5wfxzjKmq9EKtCtCN0HE4R3gLIW3+Q4xu2K1ep+V62/Uachn73sz2g7/S5bcsY/tEA/NsHwvkyyMDQezF7+BzSd/0MA5mfrCmfefE8mxvNeTQQ1u76OAxrBlk0W573p+7DAGa1BepKsIQklE3Zqrt15LQi1Fv+K2y/AaP0iYKJza77I4l6VbV7UgbKo2fVNdJ34oO7wvEWPYqx2raXCYMaae8CmA3AmyxAr25MT4auN+UZ4J8paL8PQ7DdyH2eeiJfZkEz/pZjScAN8Q0ebSCYACA6cJMaRckTRdfyfUdZ6GVL+HhR3nYeRGe9gpG4xpm77AXg2juGZb6Oo50xdff/QAkzbchPazrgLDOfBjLX3AERAzzGPwB2ejtC0FZZjCg6ciCnbvgtnrBqeiOpR4RtcAEZwomL/55EoasXgnJdQ0nEhCHWgat+XUdH4eYzMWIiytksxVrsOjkQpeFcYjmQxvKNzTZtiE8jEXNcqmj6N0NSV4DyiEiswcDwAwB2ZjunrbofgSmq+F/uNcXnLJ9BV+iCmb7gDHafmRvxmgytai8rGq8G5Qqg48FlU7r86a52y1itQ0nU+2HQAKX/mGKfZUNH4eQzPFOVh3/DRE2rrg0T17q9gdOoKJItbcypftffL6D/iGQDA1IbvT5hwqdz7JZS2X4Ky1iswVP8WBue9OKH2ckHNrm/CP3SM6bjV+kbAoLTtEoSrNmHm6l+DEURXlJkr70PLObeOP4YnJZi+8ScgAov20+9BoO8k1Oz6BlhOtP9kfIcb1XJocVhdPaWUI4R8D8B7EI2DH6eU7vqAh3VI8Fq0C/8DwJEOIvzqtWCrjgIIC8cZ3wcAhN/6IRhfOcCnISTHELzyDwDvhPOp91FR6oI7XAfP0VdDGOsGOysA3+hczP7nTXDUHIdEUQtofz+o2wmnV7SOKmu5Er0Vv0JZ6Gtwp8XFkjZejWRRGzxjM5Ee2IXma+5HcG0NpoR/AwDgHREk4uvhd16kjJtSAa1n3qlshmauvA/JYDtS/m74B4/D0JxXMHXLTeg68UF4R+bBFa/GzLW/EuuCwpEsRnHneXDHpujux/yFT2Jo5pvwjswFZTgkA506bU5Z6xW68vsu+RoI50b92nuQDHRhdPpSVO37knKeTQdMdWt3fQu1u8TF3ZEqwfyFT4JzjUFgk3DFK5F2DwMgcCZLEXNsQ2TWXoRrNmDWit8pgk/S3w1nohScK4xkoAOhqStRu+M7SAY7wLlHMDj7ZczYcCdYTu1fi+Su/6B6+v8hPS0J99gMVZgW3Ji75GGkPSNwR6egpP1jSPn64ImIBn/JPa+hPvYTxLb/Hf6az4HOqQPDucE3LAUTrAamA/E1YmYXIdIL76nXgUu0ouzXCXhP+BmExCjKmx9B2jcAd2QaBDauBAGt3XI90sF+lO46E/zQfhBPKVL73oQQ60UxqUEkep36/BfMEO9fshjzFz5pWqgYXk80eUfm6oKi5gs2FQDv0pjETngzxKCiaRBWxttGgpXQg6uVkK0UmVQA8A7aF8xyzVaWQ5UHPo/uYx6ejGHqULPzmwAoehc8DgCoX/NLpLwDisZXi8kgMra2voQzZ34Tzmg1PGMzJSLeets9f+GTFnEb9GMQSd0PlmhypErUHwaXSHe09pARTUXdZyJe2nhI+gJgmhPZtO/QEU0CAzDmjZsrMhWpgLppqdr9FfQf9U/MWHMPPJG6vAVxmSj0DR+B0KS53U58HrIjmerX/BIh7xtA/cS/19KWyzEy8x24x+qQLDJn/pKVLzLoBDfSWhDKgE1bOYURFHefY0k0aUkDI2asvQttZ9wF/8BxmLb1JuU9qGr8Ikbq34V7rA4z1t0NAoJg36kmC5H/JtR+14l0HQUTJuj/lajocrX5QIs9SJeIJAYTZVDV/E1EKrciXnIARb2nwTNWrwTPrdpzDVKBHgR7zgARWCSD7SjuPhspbz8SxS3g3SGwyRIE+04FAAiRfoB1gniKQYj+vWQ5P+o23Q6udwcCNZcDACqaPouKps8CAOJv/hyeVBTx6h44p5+OdMv78J39IwCAJ1yPGWvvhjsyVfkmZqwXPQdqdouWD4kdL8B9zOd1ys9g/ymm+zJ9i2pt4Y5OQbGB2GKoE+WtVwIAirvPtr2/U7f+AF0n/FH5LX9HlFD0LrwJ245agMumfdO2vhVSzctAE6OYhQcQLzkAhvPCHVGdR5y7RuGcfjociVJwnhFLYk9GYPA4zF/4JAQmmXP/Ffs/A9+wvTaPgJhk+mwgYDRy/cTmqvLWK5Vno7322u3XoufYv0+o7clEWfPHMTzrTdvzJZ0XIl7SlBPRxKT8KG2/RCGaAGDK1u+D84zorMDzgX9ogXr/bBQH47FsN0L+JgCAamQmbnA/0u1r4Ko/G2zZbNSt/zmi5TvhebMXrpM+DQCo2vdllLxdBWa2Gu+E7U1iyg0uEO82FH32HrhiNWAEF8KvXov0bC8GfmQvl5T9uwKVVaIFnBAbRt36n8EdmY7kxmfhnH4GuN4GgLH/3j8KOKyIJgCglL4N4NDZax8mcKajSHdtBtctZq/g+3cDAGLr/gIh1A6kYxBCqjlubM2f4JrzMQjhXrjCAEU74qvEl524fOB6d4Dr3AAwLCDwkDdjPACc8h1wPVsR6OxEir0PKT4F4imGs/5cCKPtiDu94DrWoXatExCGEMa1cEw5CfzgPtBUBKkFPFxzLgEARF67DvXkb+g88Xco6jkLrngVXHHVJnzeokeR3r8Mc9r+D6n9S4DjxePx9X8FW3UUfO81QCiLIDKwBzQVAUDgmHICnPXnoTRyCtId60G5BFwd6xHm34D76M/CNesC8VpG25E6sBBCqBM1wmXw+S6CM1EJV7wawYETAQDpjnVId2yA78wbxTojrYhvfASeY78IJlADJlCNVNMSJPe+CXAJOOvORLprI9LeUnjP+hFobBCJroXgm5YgOPUUlE3/GiLrrgMYFq4jPglwcUSblsB9zBfAdm1C8XATYsJNAKUgoKgEEMOP4D76M3DNvRTJ3a8g3b0FjK9CesYUqeal8J17B+DnEF15n/i8FRDI4VGdM89FuHUlAAJQHtj3FlgAiY5HQLYXgS2fo7w/ic16QTq2/Ffq3yvvE1veWQZn3ZmIh98E19OA2v+IptGM8wX4KuYh2n1H1ve2fPuF4IJxeEdE82zCuUEdqgBitGgq7jp3QkSTvMkivAuUTSmbITYVhHdkbt7ZXwglcI0B8xY9hsaLReHNHapH1b4vwxWvRLyoRSFNKKcXvpi0H4Jz8oLlFneeh+LO88HwzozCTVnrZUgFOsE7Ykj7zemFa3Z/3Xoht9k4eofnI162D4BEFPn60X283iVh2uYfwxmrQss5t+rH3H0OBDaJ3gWPo6jrLLgj0+3jXWXZuLKpAFyRqcpYrDB/50Zg5jdRv/aXIIK4fNXs+iYG5r5oLVwZ+uQ8egKPUHYSQt4bIDDwDx2DaGWDcmjGul/AHa5T3zHD5tsZq0La14+qvddgcParcCRLxPqTaGVUt/5nIIJT54KtRXHXeQj0n4SmC74/aX1SChDps6HpzLHU5Oc5EWhdZaZv+CkYzo22M+/Mub48hqrdX0GyqBUlnRegtPNC5bxv+EjEyvbk3F5x91kobb8EwzPeyblODqOcxLb0cEemg3rFDYIwNn5irHr3V+FIlGFk5jtgOGtfLd/Q0fANHYWhOa8CALyjc5H2900oLICyLghOuMMWxFGOygL/4LEITV+m/PaE6zFv4ePK7xlr7wJlOESX3o155DGAMiAgiLz9I9B0DPXbvgTn3AtBKItwzXqEpqxCrELUmdbsuBbcvmUY/NzEYt1MFOVNn4TzL++g9/dmq2j/Gj+K/p1C7+/SoF6g6CUW/mXiXEQogatNvI9FL7LgailKnuEARECJEyAAEQhA/o1iXxkqpp4PfqgRQngjpl9zG5i0H55IHcKvXgueWQ9QASyACPMyIKRRdIVoVUOcXiT3voHU3jfxdFEAnw5H4WMcAJ8GCCPKQCCavwE4vHBOPRnprk1wTjsV6dYVUGTf3u3ge0U3/8jbN8N1xCfgmnUBPOEZ4IebwJbNBuWSIA43ost+BSHcAwgcAIp00xL4TzgORT1nTPi+Rxb+BIFL/k93jArqpjwwcILunEL4EgGUTyGUHsSG9mdxat2XwA3sQerAYvB9OxD8lF5m4Ab3A3wSjuoFotze+A6Itxy+mefqyiX3vAbGXwkAqFv/cySL2sANNiLdtBhcTwOCn1Iza0UW/xw0Pgr30Z8GWz4f8xY9it6jH8fYlDUZr7m85ZO63+G3bgL4JEAFBK/6W8a6uWAy1g4rFPWe+YESTRX7P4PR6UvBeUbzqJWbRFOz56umY8GBkyCQ9LiJJnd0qvJ3Ufc5GKtdi1RAH1fPVj6VMG3Treg8+b6M/VTt/4JCNPlG5gIAuIG9iK9+EAAF174ajrozQVg3ioqnIdn4FlKd7eCCNeK+i/Jwzf4YACC26nfgB0WFB40PQ3j3BcSTEQgRcf1zNsVR+10nwAID9/nhHTkCCfcuTN95J9hUMaJrvo9oyb1wzjgLyZ0vgS2pRzwVgRDuBtcufRc8DzgOO7rlkOGje+WHGRyJESQ2qhP6j6sq8UD/APhedbPyWiCAVT4v7u8fAN+/E/F+68CqiU2Pqj8EswuLth/wIo1BEyGk9upNvomgCmVct+rikNz5MtJtq0HTIvGV2PIUpuMWpJqXI0WXwDH1FDCeIrEN6kBy54vATtGEMhYbAXG4wPVsA9ezTWy7SxuPhoLr3qIQJkYktz+L5PZnTceZ17tBF5QDcy5GYudLSLetBHH6QGOi+Xty9ysiaRUX+4qvE2NTEF85aHwEoKI2Nd22Urxt4R5E39X7A3NdG8F1bRR/CBxSu/+jjmvbvyzHK19TcvcrSLUsV8bDRzQEAZ9CbNndtnVlpFvsJ2eaHLO9Z7Z14sNI7VO1I7ImgqbCuuedCe6RSsxc+ytwA3uByiMQ7D8ZY1PUmEwM78xrTEY4EiU2i6x4X2TXuaKus1G5/2qTy6Wx/rTNP0aiqBmDc+VnRyCEe3QuePXr71L+Dg6omSL8/cejrPkTGJ4lfid1G29HtHynkvHHGa9A87m35HRd0zbdiljZbp12ilAWxd3nYKzaOuaUf+A4RCsb4IxXYsb6X6D3qCcQsiCa7GB0PfEPHIea3V8Dk/Zj/8euBSBuNLWaTqWs5II2fcMdUlpXVfhieDdmrfgdHMnizP1nIU3mLH8IsZJ96Dj113CP1WHalpuR7tkGZ+3xShkXL36n2nTe/uGj4V9/tKW1iSNVjIrGqzEou54mS/RjMrrSTRD+geN0rq4yKKj+HVt3j2680zb/GOHqTQgMnIDAwAkYq1kjEVUTJxXcY3WYuvUmOJP6rCcz1t6NRHGzEp+MgOhctadv+CnS3n70HvMP5Viw9xT4hhbAE65D2+nmOctIEhDN8AXNnFez8xswItB/EpJF9kFYA/0nINB/om2ctLLmK1HefJXyLvtG59q2ZRdMPymI1pLe0Tk6gklGUfeZWYkmrdZ5INGBMspO6DHWr74X0fKdKO4+CwDQfM5tpjJyjJe8AuUb4B6TvnvJsojr2AyckKECgOkbbwfnHkXPsepmMdhzGko6L0CkYhsAcX6wgjNeieKeMxWiCQ31mNl8FYZnvq68Q57RWag48BnrJAwWSRGqVv8ItKoNBAyI4LLYtIgPQrayBoCK/Z+Df1DvYlu99xqUt3wczef+SFOTAeXTAMuA3TMAIdQObqwLscX3wH/R3Yhvegw0FQYApHa9inTzctBkGO75V6JkbytKBBcCn3wYqd2vInWgDVOWusCVU7DDInkTO53H6FfGb11ZfZsTsTMEhD/FI7DMh8gFmQP7F6+ciUSCoOouJ/rvSsPTQODdxICJELj3pQEQ1P4os+t7YJl+/iSUqCIL5UGjA0g1avTGby0EKalDpF20toagIbl48dojb/3A1A8F8KPqSlRzHE5IJHBVJKqeoZp7xsUVGS7dmkFeSkVEWXLni3Ad8QmkW95H4NLfILHlSRvZh6L8ES+8p55u2V74nVsQvPx+y3PR5ffCe/K3wASqEd/8OGhsEOFXrwUIC+IpVmTSyj1F4GeXAx5Rcca7wpi2+WakPaJVbmzfdmXD1h89gOjiOyHEhpR7SAUeXOd6sKWzwARrEF/3J4BSuOZfidT+9wBA2Tynu7fAOUVUxlIugXTbKjjrzoQzWQbnQBnS8XWKfB5591Y4Z12AdMv7yliT258DAPgv+Q3CyQzW1xZIbP0nkFYVdJF3b4HriE/CVX+O8ttzynfAta+G54SvKvfQf/5PTW3FVj8I31k3wTs6B+VNVykxqNxjM5AsUoOzuyJTkAqYwv1OCJX7voiB+c9Znitr/jhKOi/QzR8AMGPNL9GWg7WjI1GC2u03wDc6D67IVCUWWbDvlIwWTSJyVJ3ZkO6ZrNnygTNZiplrfo3Gi75lHUpBQv2q/8PQnFeVECL+DJZvWtRuvw6OZDEYXlRkxFfrY3QpJI8EGhsAH1M9FCLv3iIqv3h9plOZdNKCUAJwwOwVfwAAhF+9FqngQ5LyjEIYbUNyVHzf+CGxfh/LosXpxOmJBPhwGI7SD3/WufGiQDQdLtB823dWlCPKmDdlQyyDCMPgmaIgvjwWNp1/3+fDeTFr4WK9x4PTEhMLIKqCitoeCVz7WoTb1yq/kzueh+ekb8I5/TRxUdGAtyHHJgPJnS+KpJY8So0GPdVorVGWiZ+DDipk7KvR6cS8dIZMY1kQJwSPlhTj+yOjyrE/lZbgRs3vXS4XVvu8uHZ0Yu4p6zwenJ5IIE4IQgzBNEBjtmB2TcoH8kZVthyYueo+9Bzzd0SqN0ntS2SY7GYhyNo+arlAzlh7DzjPMNrOuAuOeBn8QwtE817BiYH5z4MdZiCLqcWd58GRLMLuDb9EdZJB+Tl6wYZQFpUHPqsQTc5oDcosSBkrTN1yE4jgQOfJojDqHz4KbCqoFxpsiJgZa+8CJQKc8Uokg+0KyVK19xoU9ZyBjlN+Y9tv1Z7/UVNLGwSLysar4UjaL36OeDnKmz8BQROk1DcqWq4ZtXzORLnyN8OJG0vP6GwkSpqyXp8W3tBsBHtORUXTp+FIlSDWulJHNOWKQN9Jyt/lrVcoRJNxx1/UfZaiGZsIfINHo6z1cviHxQ1rQCIFmbQXgjN7VkRXvArlGtcBl5S+2j+0YNyB3z2jYnBvIjhNJBMgpi/3hGfYBsL3jc4FRufqiCZQgpKu8wDYBbDV31/f1umInSASH1oivLj7XCS2P4d6/71oPUv8zsraLldJB4humZGqLYqFUu3268AIbluiqfLA55W/mXT2iMelrZchXnxAH9hWIi7shG3noiYEqk5AedOnAFDLuH6VBz6nftcKETJ+uzl3dKpeU9xzOkbrlliWdUWmon7tL9F07s1KLBYAqNnxbXhH56DvqKcQK99tqle37k7FEpkbliwKBeleaOIgFnWfgbEp6lrvGzkCAHREk/KdK9dufS+LevXBWxNp0WVdRvWuryvvmvxdidc4BZ6xepS2XWK6/67IFPgS89UDBnc8+bkGBo9D3bo7MTp9GYLLS+CaUa8vRx26OQ0QidJ022q4j/4MhNigunkP94jEgRaUV9b71G41TmXk9et1xRxD6r3xrWPhXc+g5y/2ckD57xxwdhAwKYLE0QKGvys+l6qfOcGGCYILWQQXsgA4FL3oBlt5JPiB3RC8FAO3psFXS32tYZDYILpSO/oJyn/vgKuNgKSzbzJ3lM/EMUPjszjj+3eB788eDaPfW4Kq+Kjyu8Uprnl9DgeW+vwaoknFGp8fp8ai+W1qBE55PqZnaEBGRV7SWqaKrvgNhNE2xFb9Du5jrta3QXmFuAEA5/4EnPu7wJ3diDnL/4xEzxZ4ao8FBcXa1qU48v0eyLQegUoayTC+WzK071+6aQmEsW7wA7tBj/syXDPPBz/SAmG4GeFXr1WtojQWVjQxqmtDd30Lb8coCaB4JuAJzUJJ+0VIewfhjkxF9/EPWdbh+nboftNECMlt/wLXux3gEqCJEOIr7wNYl0I0CaNtSGz9JzwnfAXRxXeCKa0HWzQNvLS5J2BQ0fRphWgK9J+oI5ocyWLMXPN/GJr5ukbJaA+ttbq8hhvXo7K2y5Dy9SE0fRlmrbgfgiOBlopHQOZ1glBWN3/Ur/o/UW6zIHeKus6GNzQbbCqIkRkLES9tRPXur8E3Og/SxSnwhGdkHPfOkdVwpnPd04gNV+77gi60iBWKus/SKZC1KGn7GEZnLLatm80l2hWvQs3ObyIZ6EL1nv8BgJxiJRX1iqTv3tAGsEOtqMlY2mJcifz3QdGld0ueN9DtgbX4+4JP4Nqdb4AnDFxE/GJpKmVZ9qOCAtF0mEArhg5IJnYvBIO4OiwSSjFCsNHjAQCs9PksiabnggGckEigSBDwUjAAlgIxhmC11wtKCJ5EMR7uzd0CIhuu6u/DBfPn44caMkNGouFp8IP7kG4zZ2950+/Hx6NmQeH3ZaW4eVgfUyVGCJKE4G+lJajieHwzNPEYHjFC8MeyUgQFAf0si1uGhhGk4hN4x+/HGwE//tqnj9rzsb5eLK5Wp7LFPh94AlwajWGjx40TE0lFCEgBWOQX40NEGUZ5hjJ6eA61rPiM/1FcjC1eD2o4Dr8YzExErfV54RcEfC6sT9t5Q3UVqET0XF9TjXKOR7HAo9nlwk1VlZiXSmGH2w0KoIbPXWPKQZ0gXg0EsNHrQRnP44DLhX/SIlAAtxEqEk2QzeqNRJNZIzp3yd8wXP+OLgMKANQ23IBg/4mgTFoJiM4ILkxt+B6S/i5wniH0Hfkv8BD9rIdmv6qJb2O9kXOki+BIF2Hukkd0i3xp22Uo7joXJBpVXBNrdovWUO7u9yzbMooI+Wh+AoPHZTzPpPymwNUy2HQAzoSY4tehCVTJCC74Ro7AtE23Yqx2DcpbrkTKp/++Szs+po5X82zmLH3INm4YAMxd8ggi8QEUuaZZnp+65SY4Y5WW5zyhOaje9XUU9Z4KSnglqH0uMV8IdWDKjhuU31pBPBMSDc8Al5iPD/WuR3mNZkMrvQNM2otZK34PhnejvOUTOHDhDebKeYDh3brNcHHnBQj2nopEcSu6j/0L3NHaDLXN8ETqlGfUu+DR7BUMmLP0IaT8fWg/7ZeT5lZAOI8ujkb92l8iGWjH2JTVYHgPIpVbUXHg0xiY8zKKek8Dw7vhXtaFdolo+te8C3EOXlfq86Pt8EenqkSG9GxckSmo2/ATsFwAxd3nKJZfcuBOGdM3/AREcKD99HvAptR3ecq278MzZnabcsT12RyrGr+IcNVGdOsyKEnziM27yqQYTN1mtriwA81AMI03e1zV3mtQceCzyjtLBAdKOs8XBXMbLbUzXgVXvBrTttwMgTETGd6xWQAkLe8VCaldMcOWOzJNcZmt2fUtlLVeidYzf2Y/QBtrMWUssUq4w3VIdW+Hu1Y1mRr2FGNmhnrpgb3iPC4wYKi1AmMg0YEZAVUjnmmT4x2bBe+uWUgm7JNizFz1a6R8fQi/+X2A5+CoEYPO2m0yJgpCCYr+w2LsMzx8Kxg4uwjS0yjiJwmo+J0Dzh71vfTsYlBzixNM1G4douAHRFKRiRNU3y2uxYKHghj2Pe4Dubvo3nHWdXDzKTyx8NcoSquKzesvuBkPLzNn/tpdNgO/Ofl/8M+F1slpAOCPx38Oe0tn4Ns7X8eJA/vxx+M/j21VcyGAoHL/mwAEeEeawVAOKQJs8HiwyuvFSd5pOK9bJC5+f84PERQEPLz4N/DQg5OMOt2+Bs66My3P7QttwPxiMaYU17sDbNksCGOiFQ1NjCKxMTcXrPjqBwHWASY4Fag9EdHEIMZ6fABG9Cai44L6TiQbnkFq75ugSTUzY2Lb0/Acfw0on/vGeO6iKEa2BDHjLL2L8pylDylrv24EnHXbWu8NADqyCwDSbauQ7t4KpKMQIr3gAMBhrVCgMPaR332b/b7ogiU2Jn4b/oHjdesRAFTv+V9U7v88WE5MX59qmQbPnF6Ti6UcgzbtNsv4cpxYAAj2nwzeETHIZuI4tMozO7CExXCiX0kwmQvK2i7PWqZ257dtiabqff+TkWjyDR+FWIVoYGB5DZQFQx2YuUadH+TYd7lg9+gaeBNj8Lr8KE6NP5TFW/Wn48pWs1LvvbpTcWm7aG0ljJmDzv+xahp+0C/G0VxduwANFXMAAElfOX599g04pnMTnq2uHve4/htQIJoOM7zjVwNYakWk+8rLLBeZF4JBnJxI4A9lpQAhaHI6cUIyiVGGxWavx1T+fwcH8K8K6w2iFnFC4KUUfyspxufHwigX1NFs9Lixx+VGf28P2pyqwPeW348ro1H8urwMvawDZw034HMQKYi3gyU44AAEEOxzObHT48aF0RiGWBYNHjcW796NuTXV+FNpCYp5AV8dG8PLwQAWa+5Hu9NpSTQt8/vhFnicGdcL7c9EI/iyX52wm51OuCnFveVlCjEDALdWi5pcjyAgIVmSPVZcjDanA/dI5E+cUtxYXQUKgKUUSance34/4gyDdCik9P/nslIccIlC3bykfsG7sboKY7t24UnpGWyRnlGvw4GbqioxleNAAPgFAddJlkc3VlchrRmvTDT1sCzeHB0FrdFPYkMOFkMS7ZVgGGz3qO+B3M8NI6OYq7Ggur2yAr8ZEE2gX45FsWf6dPQ4HJjKcbhxeAQbvB6MsCyGWYmhl8bTlxjCMZgDrnsTHBVzTRs0/6AomGstOxjeY3JZqtz3BRRJgT8J79D9D6ha/Wmbf4yx2nUo6bgQpR0X2cY+mbr1B7pshAzvRijRi2KPSBYSELCcHwIilvWtYaaa7OAbXIDi7rNFTb9m81e962twRfWB713haUqAfADgerYCOl5K309/eD+qgqpbkH/4KMXc2BWrReS929BfXYaq49RNdeSdHyNZTiGxghlJJkC8X0a3x1hqBIt6n8FVdd/NSJwRqFYvOgj5Cf+xlQ8oC3ug/wREKrfhr8d+Ct9bfCf8H7tHKdfS9gYqWpZj1or7Ea3YqbPQSaX084VvRLZ2IGAlk2uW86F2+3Xg3KO2pvBGmDKkGTY24vsVgH9oAeYuU4OwT9mmxj+a/tRn4arXB4lM9m2Hu/pYaVz6Z1S/5lemDb4xlbYyPi4AT8gnmvB3mF3AxCFTEEIwa8X9ivm5FWYv/wMooSarKFe8Eq54JYIDeuFRmwEmIahuzm8JUfzP27fB4RPff+WtVr4Pipkrfws2HVQEd+vx/AmUSSsa45kr7wersWAK9lsL5LNX/h6Ryq3oOuGPcEWmSmVPwYy1d8GRLBUzeZ5wj2VdZajUmqg3ZgeUNcAq0WTx7o8z+DUBo7s/8xY/itAU0V0oG5m7sudVnFvzOdvzNBECG5LI2DEosQ6VvilrGXNp5sr7EC3fif6j/gn5WuUYScXdZ6Nm19dBiQDeNQZPuB4AEO57Skc00UgpEASCfaciNG0lvAbXx3T/Ljgrj9Adq9n5LTgSZeg8+T6UtVyB9wffQbGrAiUuOU6k+R4nG9+Be55mc2X4dvfueA5HHPNFAOJ86orVIsyJcQe5ngbEVtwHfrgJBwv+5Qz4Iorg2yyYhPgsSszRAgAgA8lkD7nNbHj8qCvwjd3mcKk8wyLGePGVS3+GV98UU9L3eUvBM9ZuyGMuPwZ89paz/3vpzzDoLQEAsFIIA55hIEhBvwfmfUIp64wNIjCwEw/MKgHv8MA7OoDzunfgB+fdiKi3FFEA3z3xM3hss5hVeX/xVMwNdeGm4lm4NdKG2jwUbVZId24yEU38iGjdtW14GWL73sLRZWchuftVy7AVOYHyAKeJqypYEDOTxKNpSSYASLevBhOoRHJf9ozEMhwCReVAUvReOPJKeDzivGwrY3DZLXzFwVk8q3Q0exkAdDcF5tg3rZ2vi7rNcbe0rvmqQtF80+W5eCTZi+W9L2BWuhjzFpsVQ3EuDK8jmJOFP8sFsKr7OfSne/GZGT/MWl6LzmgjQmurML19DNGLzXOf3sLabh7If06ZsvVG2/VsasP30XX8HxEr3w3f8JGm89F0CPtCG3BShYWmMAcIVMCAz4dvXHwd/rLsdwim4vBz9gqcPm8JqjXWkjIeOv5zaCuqwQ3bX9UdXzX1GIVossK7Z/4QF638CxYMteBXp30N9SFRCRF2+hBx+bB21rm2dT8qKBBNhwk2eDy4NBrDeg05tNrnRUAQ8EDTARxZY82ILvP7sMyvCp3yFMHZzBUHOA5hhiAoUGzweHBqIoG/lRQrpAYAtDoceDMYwPdGRrHf5cI/Sktw+9AwOhwObPO4scTnU4iWBMPgemlshFKs83owKFlkLfP74KYUV0Ui4Ggae9zqwtPmdOKJEjWmS1QS9va4Ra11k8uJQdYsuGxzu3F8MokQw2Ct14PLojEMMAyWBQP4V3ExCKW4NBrFSp8Pa7b34OKiYsQJQR3H4R2/Hzs91vEi5GuRsclA0gmAQvZwGtInLtV5KRhUiKaQpp1Gtwt3VpQjxjCgUhv7OA6/qChHv+H6EgyDJomgqpNIoDcCfh3JBIikUJwwSDEE27o65fjqOSMhjUWLEMvij6Ul+MHIKB4NR1AvEYgdTiduqa4yNyLh6bZ3cVb7VnDdW5BuXgYyXe8OQaLlmL/wSfQs+AfGpqyGkLImdlyx3AxfHbFKVDRfZXtejlFiDKgJmGMUAUCKsHjmzCDObbgLRx53l+5cWkjCybjhHzwG0YodprqZAssGBo9DUe/pcCTK4Eyo+qWSrvNt68jgh41uCflJlZRLontdLbrXVYMt34pIehSzk2NARHynrRZ7Sxjeu0RqFAlefX6JxJAiVOYCV5dPJ/zNXPUbCI64ZawfQPV150daFSsSjrxnchkApejyV+CIRKXGBFwKGi+ZLlc0Xo20tx+kW3yvg32nINW8FK5ZIglT1Hs6Ur6e7EQTJZi+6Va4orVIBjqR9vWj76h/IlfhTEuCsGOMyXWRCmbBOdB/AoK9p8EdMVuX1a+5F2lfn6X7JAGDygMZSAWI7qbORGbFgy4rng2Gk70oc4vfcOSdHyNw+QNSJwL8awJgx1xIgkH6zQfhkdwzqBKfhSj/ueLZNX+OVJHut9bdKmtd6VvUWiPKxAcgfmlEOyYjBGthevqmW9F0wY2YuvUHUj8lmhbFoPljNRtQu/Nb4F0RDM98C6Wtl6LrxD+Y2iptvRSe0Gz0HPfXnK9LtlqTCSg25de5zjkTpVg/8BZ64maChPIGInvTVLw67RRctW8TqqeYiltOR654FYRR8eOW5xdnQpz7tXAm7XXtVPpW/UPHmOpJAzUdkrN3yeV5+lss6X4an62/SWrT/Ly4zg16oklzQT2Lf44Rtz3JCVDwwwcynJ84CEdQ/J8PRjRPQ5QFKngeG2kUe8+6Dvettg7WnGLVMV5/4Y9QmhQttwc9RahIqAQGT+zJz69e/BOFZAKAl+aej+MGm9BUPNWyfNpXgZEZ5yu/Xy+bj03VR6A7oM4B3dPPwA8CU1EbHcLHW8Q4LcneRpCAKPv9paQERYIADxUwyrL4dh6hBPj+nQi/ei38n/gLGNapc7e7okH8tpLdL+XcXkZIRFWaF4kBSgiIJCc7JcKs3eFAiSBgsc+HF1ta8Gyl/VwoK4Kz9ZncaT/+QendsEK6bRUS047KKBMkd7+q/D3sDqIsGcYWrw+U8jgpkXsWOwXUej7mGscssyDLCAwcj0pJudc0tg0osiwmgej+s0KECyElxJFIWd+bFX0vgR3pxMcW5JaNMpoOIS0k0RHdh5I85L/hVA8AFsWvOBC9WCQopzR8F71HPQHBGcOslfejd8FjovufXbMGudY9oGfsqnd9DaFp7yNR3KJYtRsVEgCQSI/B4ywCw7vhitZaumz7B47F/rHNGE6aLUSLus6CM16FoTnWbpvKcCmPMZcbMacHX7/kp3AIHK5uXIo3Zp0FN5fGvxaqStzHjr4SA94S3L7paVM7zmi/aa668bwfIJjKHOsOAH5+xrfgS4v7v9aiGjx69JVYOl2V9frHEqgqMht+fFRQIJoOE3Q7nQphI4MjBG8FA7Cafve4XBhhzQt4r4MFksCYRYwnGXdXVMArCBh0OPAERLLntkonfitZtPy2Qlwo5PHI4dMW+33Y4LXXfFNCFJJJxlKfDwFBwBKfVaphewzYROh/pLRE+dstCHBSYJVPHRMlBO8GxM0mBXBnZQVAKeo4Du3O7NoEI/6vvAzzUikgSwKeOMPgu9VVmMJxprFbXUt/lgwE7U4nfl1ehg6LciELAi5fdDkdSkyorRK5t9ftxvU11Rjtzd0tIE05cD1q3IGy3efD4ajC4FxRo9ibHsN0t7qZ5kKiFoka3CvYLIGkZYRS/Sh1q6SUMyaSYC7JNalu/c9BLVxDAIBYWARSAC+fHsTR/zFf82vtfwEBwWfIDyCwCQtBI4PkIS3Wip+91XjkP0yaT73wNJrqRyUq7PuSEBnajUD5UUA6jjMbOxF3ObAZCwEAswEUvxdAYMolKGvNbiptOV7DIhyNduVFNHn76jF7+R/QdP4PAajkIpP2oWjPEbb1dGb8lq4DVGehaDVmOf5RsvNNzF72Z3A7FiO59zmFaJJKZxz/3CXihovhRYHBMVyCsGNjxjqZYdGfhcuClavW9I23iVY9yVKdpdHULT8EY2ENtKznOVxQ+8WcRhXsPdXkgpkNTs3mgybHsDm0AScVnwohOoDif6cApCBcob/ef9Ycga/veAG40F5jfLDAG+IzxJJj8LmL1NfLVgjXf5vF716FQNUsIEIM5IgcS05syJEs1VktGt1kp226FSAcEkXtKG2/GAzvRg9yJ5qCvach7R1CafvFAICpW29CpGqLkj3ImahAd8xMkOx4/AhQSjGj5xFEaBweiO5bi0LnQZjtBx1agmKXcfNq/Z14InWYvfwPYFO5zeXU0I7xtxF/m3EKbtb8FqgAxoLE4GgK/2n7Ay4ovgyOKvNGVIgaghdTiviGRyBEB8CnIoDbh+U9z4OC6r6Zm6oq8WC/Gkx2gGVRabHpfrYoiAqOx9KuLvxaCgD784pylAoCbh4ewV6XC05KsdPtUmINTTRGox3keIq5oNXpwB9LS1EkCDglngCG96BCCowbJQwchMHqMpUAooTBE0ddji1V8xF3elAkua0Y52K7ufmeU7+Gfr+eeNxUfSQu/9QDOV8fCNGRTDIaS+vQWFqHq5rE0A2cwKPL6UANz6PN6UBYI0P9qpzFj4dH4KEUXQ4HKIBpnNkaqbFkGuaNivJL6/JfoDJuDl8xmRBCHXgvvRCe9m2AD9hUNRczwh04smsQU0ZFhc/bAT8aJIv1Vp5Dl2SFbsRbfj/eDAayE002kJWQb0seBl8ZG7MsZ6XM00Ezfzokq5MVHjf2uV04SQrrcVdFOX44PIISG1JfBwvFjDySzL9V7Ol6A7OLjs/eVwZQiQCMp4bxatuf8akZ3zefN1hO1q3/KXiXnUW9ON51/a+jNBhFbpFAgRNbe+FJic/fN7gAIDzGmkvgnTIX0coGEAuX4/6GR+EKTEPJ7MtM5+Ys/QtS4VFAs+0r6TofvuGjTBmItYituA/k9G9bnNHfg2lbb0Y/FpmW25ald2MmxOy4a1/pwJUnfgt2ECDolP8c48AzR1wCITYKF+FxU81UXB6NYovXh8Z4OxYkrEOUTN3xFPawHrxbPReX9YkZQfeXTgdDBfR5S1EdHzHVaQ2Ke+SEw42EQzJiIAQvz71AV87tnNykMx82TF7u5AIOKf5cWoJ/FZlp+DcCAfy+tBQtLvtsIVGGMRFCYyyLX5aX4/ZK84Y2JlktZSKZ7JBiCF4qCiLFTE4mAy2SDIOXioImix8TCBkXyQSIFj1L/LmRZAIh6BxnP1ZodzpthbSJ4uVgEL8vK8X1NdX4u4a8mwgu/9QD6PJOQXmLauq+OdymKyNIAdp9wyKxMHXNdahb/3MlRkg2GBek4MBJqFv/M/TvCeLdpj+CEVxgOevn5UmbBTDRtYXib1eap8KiyBg4mgKhDrBcAMN9elIhU4wmb2h21mtRxmAgF5xd+m9TCeadBaNdqyUNK0VJPInakF6oJGmC8paPK9nPQkNqUNaBTbWYvuEn+vKmgeY0DHtQYmkdM3fZX1G6+mhzeQmJzY8j3bMNkUWi25hgcd/VY1T3X2DIkGmGUjjSQZBx6FgY3qOQTCbk+Jmu7RdjFGkzsAmJMcQ3P45022r8qDo3kdI3ciT8Q8cqv6t3fwUlm76KwODxJnKzdXQryprNgWxX9r6AfSEzUTZl+w2oX3c31vW/gQ0DZtfUFb0vmo4ZsXF0LaLv/0ZMJSzBSCSMuv1INy1WQvpkimc0WXAkxI2/d4/BVEeaZ+Ux2H7bBqIptWcE5D/vYEVqFADQHTWkrM8xVox/+Cj4h45FecvHbbO0ZQIBo6vrTJbp4rMBIjGzy6nXPIcjVZjVOQquezNeq9ITHU6BQ+PYJmwcNL4D9i+7I1WSc+w649ImZHn+C/0qgfV8y2+xZP8fbMumhSS6m15V3DkciRIEFxUj/Oq1eHHWGUjsfAlcjxgThibFLKtCqB2CNKiKAxtReWCTrk1eM+Drq6twZ2UFEhbr8wqfD/8pCmJ5UiV4Bh0O7He5cH1NNf5YVooHysvwnt+PP5aW4LdlZXi4tATPB4P4W0kx/qhZj7e43birQiXzfzEqbnaGMygSZfy+tBRPlRSjy0apFREEPFhaik2SlfebgQASDIN+hwNvBQMAISC8eA19DhY/rCrHi44E6tfdj/p198M70oQX5l2EAyXTkNy5EIG+bQDEEF1/Ov5zePwokdx/f+rxlv2vnWIdk3AysajuZABAR2QU/yoqwv1lpTqSCQC6nE4skrwCflNehns19/vRYvWdu+Os7yh/C3wa4HK3wGkp0sfoWzzd2r3XiNXCOnC8uI6/PussEAAzB0Nwczz+UFqCBrd+rlihUbqu0sjr70vHf11ehvf8Pvy1pCRr378pV0nAHocDN1RXYa3Pi7W+DPuADNZrgBjjKi3ND05JweYyzJF9Dgd+JT2D5J7XEVtxH2TJzegFkE0omdLwXbFfXk+2CpI7YvvgOpxxwBx3xxr2fVFFOUiQFFQLGHeoXilhXN+8obmWlvdiM+I9EiCAM1r+2sB1gKAmFEVJXHwvp2/5MaZvvg3rBt5EdOXpmLHmHsu1hY8PI9y2FM3h7aZzLOeHyyJBgDNeidK2izF1602mcwk+mpfVJ6VmFQOvcZHk0w57ucsCRd0bUL/ufoz987uY1vA4EuDwit+NNoaHO9aveVYWY+ETeA2iEurl2eeicv/rAJfAE0dfYSr743O+i1vO+W5OY/K7CkRTAR9CUEIstfsCIdjvzpyS1g7dTsekWMsUcPiDJwT7M5CR44W8lM5YexdmrL1bibOgdiwu+L7R+Zj9yi/haSnNi5SxWuy9oTmY3t6M03eqxMlIsg+r+vTZRRiLGEHDHjGd+1CR+Vs67UA3LtnRrNZP5y5Yxgeyk5Oym5e7R+8m4OgjmLvoH5i26cdwRaaCjZcYao5zQ26oFhlVN8Vdm0uwZEO2bCz59ds4bAismEMwcMte48NIrP8raLQfFMCVV92nO+/meEUGk0cojHYg/PoNIEYtFC8JbVaa0HGMzyll6vKMZggIoUFXbD9W9L6I2Mr7kG5bCSE6iNj794LrWIfE1qcQsdXQAnFO1X6+3v4XHeFT0nkhStutXSJ9yRRmDYyajodSA9g2vFRtf7s+EExbdDdaImbhsy/eZjpmBWGkWffbGN9Ifla+XpF0lgnQ8WAsYW+BNX3jbZi5UnxnHOkizF30D3g367P3mC1j9POB7PIhhHsQW/cQRsdEQZpSCmG4WSmfljYwciyOwGAPUpqUyvlgxtq7MGONGjOKEyZm8UJBEWf089K+4FzM6xO/EeLUWyk4MryL2bC893lsanwkSynxns1YezdmrL0baWR+/pxR3rFwm1lZdA7ckkLBwfOK61x6fxzBV0Qlx8l9e5E+sBDx9X/F0q71WBdTzZXl9WrG0BhmDKn3Y6fLBZ2aQhoLK22SZTf/bkfu7zAlBHvdbrS6nEgwDJb7fWjweLBXQx78o7QEfQ4Hrq+uwvXVVdgiZS8ayUFOk+XAX1WU46cWCkQBomv/YyUluL66Crvc5k1ou9OJHW4Xni0Kms5V7VPXi/iqp8D3N2BdzVH49Sn/g3fqT8eL8y7E5Z96AKumZk6EcTDxzswz8MmLb8MoFZBkGDTbyDxvBwK4vrpKecc2eDx4vLhIF+e0ZNczyt+C5l188sjMFsKXX3U/nqvRyzi/O+lLuOu0ryPiyL6Brh8QN7693hrdrLTP7TbtAVb4fLihugqPFxfhhaIgbqmqxMMlxQq51u504tVgEHtt9ggrJHJqhGHQ5nTihqEh7Ha5EGYYndIzLP39p+M+q6ufiWQOd6wETY7hkdJi/L2kGE2SUlZeB973evFv6T2Ts26n9r2J/tFm/KKiAn8oLcEvKsqxMye5VYozx4n3V5CC1hd1nwVHvAyxTeL9cHECXHwWyylNDEHbIqAojiWUEqHOVRASIZSu/B7mLXpUUWhqMZIaNLWjDl+zHtnc0tIn1O91ys/movxBa0KZoymMJULwROqUsQJAMh3B6r5XkQp3QuASGoWCdYdVe69BoP8EqQRB1b5r4I6afauzKRo6F92s+y1AgIszPgO1DUoIQgnV48AZNbvXCwSoW/8gpm15BKXt72fsX0tr3WYxL4IQXFc7FYuijfAP7YNnrMOynV3lMxFxZXKzVuGw8D76KOGjffUFFFDApEJeSj3henjCM5CW4m4UdZ8FAHB1qBrC+KJfAhPcPMkgAg9Ws44v7H5SDKqdBZtZBnYLKwMx0KWM7UHruBFWcXVyIS0cyVJU3elE6QYxGGVi678Qee920Ngg0rvfhn94AWauuRdMjlnDZAHioWM/rRzzJdO6EjoY1nZHepJTsBpuK8kQ0yobEqwolAowE+yl0bjJykmIDgACh6ghkGqqaQlS+99Dqsk+S0o+8IRnYOaq3+SUuQUA6gZGMG3nKtBkGDQ2hOiin4BqyLBsW/uaHddi2qZbEecj6InriRyWUrze/hdTHbvbnhT0Wt5nZ5yS0zUI0ii5sLpBz8UayTuijw9UJMU+qN58NWatuB9c736rajnBqdksxFb9DvHNjyu/fSNHItSjCvUMdZoSo8kuH3bazlTjO4gs+hmEcDf43u1obH4eu0fXIjK8V6ovtS39VdZ6GUpbL0PZcg+SC3+KwaRINkXSZvN7wltvnDzhemVzIPYhtr2i90VE0qOWdTKDgjMEbA65NMSTweW4PTj+TDl98VYkkuZr1UO8Hk94BjzhGRg1vPzGeVX+pcbzML9z4XgC1bIlJyFgpXhedECdjxnFgoLizqlH4mdnqi4eFGocHC3+UlYKSgieLQqiUWO1LP8Vl+akfxXp3Qb7JktxJykWBwUB/ywqwt80Vk9WVuhGDLMslvq80D7hX4ZG9e1bgCMEfy0ttbTUJgCCPZtQvVuOa8fj7tO/oRAIWnhC7VnHeDDgHT6AygNv5lZYcw+eKCnGRoMFvyuhzYCqln1+/kXK33trzddev/4BFA3tU35/++gLUNy5Gq2juxB1ZvYSEJIVqArHcEVDE+KsNyddDyUEG71epAlBxJAMRob8LY0yDP567KdwoHgqni4K4tmiIH5YVYlfSO/U1nQKfy4r1RFrAPDTqkr8sKoS78zUB9JOJOzJk0jPRjxdFMQulwtbPR6T1PVccRFW+9SN+0rp/r8X8GPYwSrEmnE9i7x7K5bt+YfumNfgPijPnc5kOWav/D2YMZ90XI+W8A6MvXurKD9kwdKeZxAKi/E0K0MRnNqkkiEDjS8h+u4tICAgVHTHrArZuy0meX2AdC1ZE+sT70NJpz6otHeTZk4TMstXujPS4idQDp2xfaZ3yoooeq39IbAHTswp46rRfXJg97MYGRPX/lisF5Tq1xlf2zac3KqPTaK9FIEwWN32FF5v/wtmv/8g6tfdpSt72oEupFgWDOXgSI1ltad1SJmM93sCGLOZnwnlQKQbw/ApyzWhgNxRIJoKKKCASYR+QpYz0fiHj8L8hU/CMaoXekY0AeJzQYwT4yKkuw0kkmEhuGRHM05oNVs5vNf1OBqGlyu/H0bCNGYZD5yoj2mTkCwymLRBOLTMfCIud/HNjyPy3u2GoWriFAyKggggBkem8WH0e0uQ2Kdm+wkk9QuzP2lDzkntvjXrLFx9O4vvXcdifnsYl24XCQlTTBSiLrLHdPTj5BZ9nCpGoNgwYM46lDvE/uYs/Qumb/ip4q7mG7RwmSAMmocaEVvzB8Q3P4H3Y3t0p1fI7hcWGyJCgfqw/Kz1zzI12orYmj+oB4Q0krtetoyFJAtqjng5pm65WWP2nhmuWE3O7kJHdg2gMmKfdSfkMrtDR969Rfm7uOdMJcOgETQxhjhvEfPBRkgSDO9tmNVvksZoKUptxtqw9xGMrnkQPXufsTxvhWDvZt3vUzpFdz5GcMCZqESiZZk6tkQIjaNmayo7uDj1WvjBfeA6zGmK9dATSnIsL9mlIjTWaihPQaP9yi8uHcWOkRVKDKZUWrzvYykx/gMjuFHV+EUQxe3A+v2YsfYuzFr12yxjVQYJQLQoW91vDpC6N2SdGcc9NgNtkd3gKYe0gWhyj6jkHjX8tb1yDjb7TscJhk2AfCXZ4uoJBjavbUjvvrm+Rm+Bx5rmJ/1vDgRLe57G8t7nxXFYvNc861DmS8fYAGIHOPQtPBZ0mTpnkwy7dYEQHD3cant+hc+HBzUuRc8WBREjBK9LcSF7DRZNvy4vw605EEFWsHOPW+vzIqI5F2JZrLMgE4x4sagIN9ZU48dVlbi5qhKrkuMIvmxAedsyeDUa/7oNf0DN7ufgG9oHwqvrlGesA38+7jP6yuPNypYHqhtfgSuewXIkB/yoqhI/qhLjQP25rBw3xNK2b9ALZ5gtvwBgTKJ2NnrccAztRmnnGnhDbXhsyvEZ++bD6lrpDvfAacgSN14IhOCOygr8vLICb8w6G9+/4Cas8vkAQpBkmKzhKNJSOVdELzN09SzD+70vmMq/1fF3pEKtSh8AFHfFVpd1uAlWussczPOAFjQxit3cCJ5vUedRj8lCRg874mDD4NsgiVFEF/0U8Y0ieSUrSY3uWwOJDvQNi+63znQKTkGwUOqIB45t78PMQdEyjeE8UvIGTWFKsWd0nakeAKSjTsxf+KTOVV6so7nGrESI2l7Vvi/BP3gMXAOZPQmKO8+RG0eCj4KjOSoiDWNJDDcilhS/wcH+jXBons3zLb+FI9ynG9/GgXdMsipP06JsE3aApJ14vuW3eLPpT4guvxfl0QTs1lcrOCUL44TmeoYyuCKXtS3NuGYUkB0FoqmAwxcFFvlDh0Xda0A1AiRHRG19WNqApQf3IMaFsXlQDFLd6y/Nq/0NA29hy9Ais7WSwY3CIVDLyW00NYAYpwprdqEYASDk1ruZpKWN+Yx1d2NKww3qCQuLJnnZE0IdoPFh3bnB0F7rwhKem/8xnQaRMRQge97WkVUqNN8LIegvJWAoVdw7MsVPmD4chofTX4eLF9AS2YHGkBirxJdMIZFSn5eby2x/45BeA5bzwzc6Fy2S9dq0rTdh7mKzW82GjlXg+3eD61iLfxFVA/7Nb9Ur12Y1I3B9akZAOcOfs0u9Z9o4QS/VWgdnbwxtQlixNqEIDB6bkTzqG8gvCPi/Yr2SljTznOa2jG2gjx+kxXtdT2B5z/OIb3kKiY36e9rYZY6vlAkxQ/untHbjjKZuUN5MbHp79oKmIgj0i250bmP8M0pw30lfxk/OvNZUVwYxkD00NoQUn8DWrlcRffcWjEhWQDPW3INpm3+Ucez8SDPWNr+DkaW/UI4t6f63ZQwquXel33QcKV7cdLvl4M5ZNsHGN2Nz89tIbH8Oe0dW4+XW34NXYnAJUnnx3o61LdPV84TrTZkH7TCQEL8JCgEEBNM33oZpi1VrnIbhZUhR/bjnLv47Zqz/OdYNvAEAJosmrSUgtZgf9gfrTbHe7EzkuozxqQxIp/TBk50xPQHgMPRvfNv9/TsxkOhEWhCfFTXIB8k9ryPNOjFjMITT93ehKhzFvpYX0NOSRkzroqSpFujbhikNqvWb0XIjG1b4fPhRdRV2esREGgnDhiXJMKaYQLngZxUVulhBk4kowyjZcicbjJAGAUXV/tcxY+MfULPrGdTsfBrFXWuxcMapurJ1m/6cc7tF3etR3LUONTv/jcp9IsnKJkOYuvXv8A9I8/tBkhdjDIOYdL92uxzYOjaIKVFra72Ger374WOzRZKi3enEveVleKJYT86+seEVvDbNQCBoUNypkg/ucBfM+YLHj1GWNbuj5omaPSqp9JXLbsayqceiN95iUZKis1Rvmb1PSj4TsXkXHdKlGpJi4tmiIvwzYpDcbJ+9PmajDMYgP+0fbUN7RC+XcV2bkNr/Hsp3XIK5S/5mGSdISZAiyYY10lwZSEgu1FI5lueVv2cv+zPmLHlY144zncL2kfcxnBRJfa0SUEYo2W84or0oszyY2P4cuPiQNA71JrpiNZi25UeKJS1vcf/nLnkE1bu/bu4mJ8gV1D59KVGGcHIUgZQqT5y+vwu1o1HlPg4mOtEc2a6rq31WB9bcg+YVouI2wUchjObmxq8bndS0rAe5s6I841zL8CnQETHW55JpmhiHAgdPSOyfWHhmlDe9gykNT2D6xtznuf9WFIimAgooYFLgG9qLvZEuRF5XSZi0lL1hJNGD51t+CyERwhsdf8WBsEgUWW1urLBlaDGW9jyDNE1h/9gWk2BBDZuY8eAzzmZ83tug/BYM0+Orw7uQaloCV7wKwT6N0CwthE0DuzSlMwhwJuFODjCkkil6jZu+/I9nnYzIa9eZ27UQthgD+ZSlOPhwr+lYZ0zMPMRQirNam5R4NUaiKb5THyS6N6S3SrpntAHRZfeAUBaMYIwHYn+/RgNEEzBaXy786rWg8WGEJRcEb2gudv77FATes36v0px1qtptw0uxslPcwMhxl3jYkw00T238e+kQoot+mrVcWYuFS1+GzcBoqh99iVZw7atBUxExWIEC8b3MNch2akD/vEpSojAfW/FrJHep8Vgu3d6M2lAUPGHByBym8WUiFMumn4itVRpiz1BG+b6k4w6ex4HVP8XUbe/JjQAQs5n5h46xHXd02S+R3P4cuka74BhTNeyDyS4MJjrlznV1ZKJ2JNaJyFs/0NwjKRj49lcw0Lnctk/ZrUwWWimfRLp5KSgoOK1rgPJNS9r54SZdSnQZMvGeCav7XsG7HXK8DyK6BHL1hlL6d4URXIrFJCBm5NFCm85Z4PWWmkw6niUZheF5GogjI0FpjJFnzAI6wtlb+gFARct7hiOGzZXAgWecIADKYnq30Kjb2kXJkYrAFVezEFEwiGpIqcg7P8bgwjsyjutgYMjBKuSGHX5RUa4LFC7j5WAAT1kkivkg4Al3wRPpBgEFxziUjHJrao4GI3AI9m2DK9KDug1/0NWb0vAEKva/ofx2xkdQ2rESnkgP/CMHUNayCLU7n4YzGUJF01uo2/Cg6KLWrVr11W148JBcoxZay4ceXzk2RVTFQadVcpd0HDGJHL7vhKuxpXIuAGBH+UwAAKNZZ0o6VmNN9eTH1ZwIGI1l8ICnGpzNO8vR/K3X/hMMYIPHg60Ga70wy+DhSBiNJdMAAMumnWCyJJShcuKZCbWlPeuxduA1Y20kd70MvnOjiWTyDYrJS8aiItHASRb204ZFC3K/lPlNJXjU94KhTjCa7G/J7s2Irf4dAKA5vA0AEONCuv7e7vgH+gzfiGGoAIB/HnGpcijdvBR974uKF6uMy8q6a6EoZXi3xgVOLBdJi2PaF9qIZT3PmuqokOJjSWSZIHCqlakhHlZZLCGWli2KLRWoKgTKQ1C+ifGRpHtdLqz3ePCsNEcOOBxZiXf5/mm/3/oND6J6zwuoW/97lLUuBQQe07aoyj5vqA2u+CBYQyD6jyLyT71TQAGHCgcp41oBBweECsrGU4a8hBkXumlSoNVsKa1lHBjbot8wS+2lOzeC620A170VL55F8I1xjhsABgkPwqiCU1dA7/IQF1JI7noertkXgfIpEFYU+tLdW+CqPwc7O9bhCLVV8b+8tKxqWb1Pvf4eDRoWrq7UAKa6Ki37yuRbLjc7nOyBE8BT55Thq0vuBBOcAv9Fd2nGovZfHR/RZEwjaBrbhqn+ufCwfp3FVPjVa5GYMhXPt/wWX5h5GwAgDgFCqBNWGAx4ABuvANbbga1Vp+Pijk1oKa5FoK8BqD5HVyaYVjep0b4i23gFaRtik4KCSzgwdesP4B2ZK11eDumVLRDnIvA69C6hWksSAcDWqnk4qb/RPA6DOxs/1ARYCKt2cIRK0T28Fe1cJ7wOr9yodWHDe5VIR8H17oCjRk/qCKFOpEKdcB8tur7IFnIpVmv+r+/jvbmzgCyW9nIWL23N6cMqYZzr7C+ERNcdK82sbR0ujhW9LyIRasMZUN/x0mfLMHbhIFzdYyCdzwDTzpfGyoDVCMEk4cWU6ACmSeOVrWumD4+htbJEHb1BcJb7iS6/FyEXwdCRZ8Ob4nEgthUnVVySccwcTSOUHgSI+kVGPER3n7O5cKYZFsld/1GepZZoogZxkFDeMsOjFdb0vw7a04DplWrMFoFyeL7ltzi3+vOo9c2C8YmmDWTtCyO78cmmNfCe8u2crsUYZwsAONbaDcfuyykyuHMKhMCj2TzT5Ji4uSiqyjiWDwL9Ftnk/l0U1MW5OZwwpeEJsKkIPvHJ38AZ7kL1nhdQ3rJIOV+7/SlEK47C2JRTwKbCCMQH4Q21Y3jGefAP6UnwIinDHSC+VUR6l8ra30dZlmDAhwrra44EYtnjzr1VPRvtNcdjKxLYeMr/4uihFlzQaY4vyVAOC6c7UVKSRLdrFtCcyR77gwCLQUcV5qDJdCZh5dKdBSGWxRMl9u65N513I0Ap6jb8HjNgJFazzFumcJWZyuvXlfkLn5T++i2i8T6d4oBAXR/F39Ial0EOiza+BWekD0AATeEGNIUbQKm4PJeHY5jbN4KyqPmeWl3QkEd/H2QxyJnFlTAXbBx8B5W7lmFbVTb3TXEsO9pfxNTa88Gnwrr4qTLcezT3nMgxEin8iZTJ4kxpmTCmNTVf8ITgyQzvlRUYRWFk8C6AuE4G+7cj2C+6+tevu39C4/tvRMGiqYACCpgkmAM18tIELS+4G6rmYyd7Co7pFN1iZFeF3aNrsbL3JduWjVYZ/JAowKVbV4Dr3ACAork2s3AR8hq0+XwKM9b/Do7EqDJ+rQTS5y/H56+4R1NDPBd+8weIvK268iQbnsa15ffDp7Oqyp0cMApFnlCbwUTeeF1SMEcpk4oaNNeCaNL8LTBmrd8rbX/E0h4xzs7rJ5ZkHfOmqvn630PvoSUsu67px+mL80p8KCCzW8qW6QbNvIFwWTb9JHz+invQVDIN5S0LwfXtAj9sZaJvvQFV2810TkBg4ASwnEgSlR64MO+GFnX9E291/t10nNMIR5QAd53+Ddz2NRZrj7B+tjLiW57IaegyWIHHrq7X0BbdjZikgUwkcst6lmIciG9QTfqzBcBM6jb1FE8O7UJ0+b0AgFGLFNhyuCI+JKaT5i3eh/c1GaqMLqPZwFuSiGIbVnHNeuLNSPMxqZRYzt3iQMWfnSCCvu+fnfEt3W9GYHBcR78mWYBY/sjuIVy6vVkl1qV7uG5YDAbsHxKvXRhtQzrai92ja9HcL26MX2n7o9J+crc5BpMMlheU/oxPKNsd4xgWqf3vKr+136QsRKvPnZqzhtr00hHdA97CxVJXS9sXFXBgaB+Se1WrFQ48uC7V1ZHRlBeM2SMBEMHCXcSjuiE2TrG2cmgPiqTRwrqTdRYZgHg/5Eykyjg+BC78cUa8V4kcLYQ/CMja/akb/4SqPS+azrtj/ShtX47RR78OVnJlZbkYKpve0Vn2HG4o6tlkedz62zGDB4Pl008EGAYRlw/ra49WnY9Mxs8ET17M4s1jrBOTfNDYSwJY0BSyPJerUjFXCITRyDSZ11FbSDe4OGUfqHsiCm9qWAdCqUEMJrqwsu9ltfkM9ef1DqMsqioWw2/+wKYjsf33px2vPyy17sjAzRBkjlsHUJRG4qjv7QXXsxW1I2FMHQ7jscbXke7ZZlkeAELRNqzoexG6ZyHJQdW3OFH2sEYel76VsnAU5+zrQLVmvtfOvwK1UyQcZKME6Tk6EiO46/jP4MdHX5SlQgFaHL6rUgEFFPChwLVHnYvPXfFLUEJMpuFjksCY5NWFfHvlXGVZkF05pu7biPmblyhltg4twao+1V1Hxsric3Fk1yBobAjhV68FP7jPVEaLDQNvozWyUxyLV79IDY3tBaGqLquyUTSd/u3nGNzxOdF0XZu+VNlTcnGA1wRSpQJGmShKkhqtncIzWSzg2TYuBo1NYsPfkGpermlabDy26GeILr5TFRKyWDQ9M19vMUFBkBIS4CWz9nTY6J5kXry3VFnHOBKLi+WH4mpwWK12T45SEFn4E0Teu81QV78UEY05t5AS4y5FXD5U7n8dBEB87R8RW/Fry2EsrjvZfow2mNM7jNMPdOmOFXeeotFe5oakEANPzRtujtNYSRDxvW+pJdgy26wh04HyoGnx25HfYxkV4RiObevHce1q0PuSVARTRkTCc2BkP5Z0P42+/myBsUXsD1bkFaCXgugGvDE+oIuZMH3TQwhqNmGUELwy+xzEVt2P6NK7NSSH+I5sqZhm2JxNBtEkwpklnpjfLZELNtrSYRtNsfJbdrmD/p2XjzeE+xF+9Vo4k+KzfG6eRlCV2koJCSR3vYyfkmakGq3jaxEAF+xpw/F7xbg0ywO7LMu92/kY3uh42HTc6DqnI38NDG3Fgbdz3iyLyLa5E/vih5oQee068IKAlIZoMmKYEzdY0SW/QHTpPbpz9QOjOKbDGLMEEDTzxoFqpzKikFe1MJQJ0u0Vc8xXQAhuuEAfD+zDYFf9WiCAVwIBbPEYXZIPPxDK225sCQBwOQYdPkxQZoi7JsM92mx53AjPmBh7zRNqVY7JctREMrUeagT6GhDsXInvfOz27IUPGcT3LNPaAACfaFljfzKPOfCxo680VtaN492ux7Ck59/ojh1AS1S00hPyCfBu614stp906L9/rxQ7sSRh/qZ8yRTqB0ZRHEsqRM365seRsFh7zmjqxrxekfw5ob0fx3X0I0Z5JDb+A9Hl94JyqjxsctXXvMLye81GCQhntmgiVA1YsWHgbawfeEs3V1CdYuTQQe7VkRhBX+9qRId2ZixfgB4FoqmAAgqYEOKMA1GXF2J4X71gtD3Uifjmx7FzQBLGKFU2NxwDhZxwcgK8aXWT0Di2CV2x/dhqSE/f665RsnfkgpbIDiXL3HZXD9wpAd6UtebdGR+Eb2Q/Ns9lsK9WJZhSLe8j1bwsc6BYYrTwyENANGi9jHimfDqS29XsXrIZNk1FIER6sVuyyPKPmi1XZLfAL192J96bapHtTYP08Nm6ceR6BapgQbD//VvAL7PJoiVfZmwQ1GChYCZYrO+Fb8iaWIyzauyKlqIaXPsNOS28ITaP9cgwr28ERfE8sjDZNGQXYyA1qBIwukDvhnYs4ylxSbzU+jvsHFmJIan9yxuacEpzD16qvwxTR1SC86T+RkwfDuPyhiawPMVg0tpV0QrmrIT638ub3kNEE6+msbQONCGSWqmR/frylIDl4gj2NQAA3plxGgBg1B0A0jEIY13ok4PtS896R2mtrr9M7x9Nm+MeOCyzP9o1oL/Pq5vfRYJLgMZHLYsPelVT+1dmn5OZcAWQzXV21RRzzClXmkNq/3vYSDLHm3PxAjzhYYTf+B5eLlJJxMsbmhQrsDA3okt6ICMtxc3ojjVh0+B7OiLJYcjO5UiOZQmObYi5ZPidcIt97RxdhTgXxmi0S1fPZKxhuFV/HdyB2LqHIIR7gLTe4uCo7iF40+Z5nNdabVK1k301daayVuAJo1MuAMC7Ab9N6cMHSYbBwoA/S0ytAiYLu8qzv0/bcsgECACeSDdmrHtAl71PwSF4nFMankC1hYXZhwqG+/SfCv36b5RL5Syl2RQQgOqanQten6LPCNsdlxI48GbZYkdoPV5q/b0YWxFqOInJhJvjcXlDE4p6VNIz1SzK4gwV51HtnQnF2pHcY29NqwXPMICQhjDaBppWY18aYz0CgG8FCyYMYLt1jDF+pAlCfERn4doS2YHWyE7JokkcZVVs9AMh/oed4prQ5zy8YqR9WFAgmgoooIAJQiUa2nx6zT8BwHWsgyBZzTBCQtm86DcW4o/r0oNIbPu3cvQOGtZtbo2aqTuOPC/jyC7c1YozduxCZNHP8JeiNTiyfQzVIX1AaPdeUQvExK2nw2TD00huf9ZEAUS1VjdENPEt6jobRHBofPOtiYf45scRfV+0yCHy5sjGmuT5macajuhH8q+xVkQX3wnnmDmQt0w0CYRgBBTrtzyGVMtyi1YAgODVWefYB4+mNKM7FSUENSMheFMpqX17keAX1+jdWpa6O+CwcIUBgNTIGZjS8ATq1j+otPji3PN1ZTiGRWztnxFb+6eMpu7U4D64d3S9bdlMEo3dPRJsyA6e0wqaVAlay0iX3OmvwDPzP2bbn2x1dg0dQ9eyuyVzdyDhUAWfG79WiT2lM5RzrpTYeGXYOgB6vrh/2jGgMTVw8tbKuaCxAUQW/xxjTa9axrlwJoZx+acewJ9O+LyJdBlx6t3rCAiOGWzS/bZC5N1bEFlo1poPekpyvxjDWHpDrXhxt2rm/7vZp+nOO/vUmCl/P+YqOMe69c3ZaXHl799wOmWIJVTZR3Duvtw3NAAAPgU2rZKMBKq72QU7m9Fd1Y5lfAwhqO+kHCtsZd9LaApvM8RoMr/TGV3nCIUQHUC6byfqBkM4snvIoqwYA+71jr+CkzZbxl56Yi2IpEfN9cCD791u2abYkHm8nC7Dm0b7rSNBM8xhhnfuO7VT8HYgYFO6gI8q7jrzS7bnrr/gZvzvpT9DJ597ghKZpHVIgekPuiuQBq74ILwaa6rxwjfSlNnNjOaffTEbXFE1XqQWRZIbLz8iKnhk5dzo8C4ktv4LRZKFT8DCpdqIdNsqRJf8IqfxBHu3Ym3N0crvtf2L8VbH3yFYJCEhFOBpWtmEt7jmo0hKYhBOFKN+YBQlsRyVXxZz2o7yerEfAEL/LkSX3IXwq9ci3SHLPOY6FCRnUyHtOxpb9QASO15AZNFPkZLd/aV2EqwTzj6CmttcQMiGckjHMbjkpxBGzOEQ7OI1aR95Zte/iWNDWT1+dsa38E5J5UHt578VBaKpgAIKmBjkRY6I8QZsCin/yZsXK5GklXJIt65QfidBdJtbc6tqKzJxldb42/d7yuFN86DRfvCE4v6Tvoy+oFc3bO8eL8qfKAeTyjwdUoPK/Ws0pJBFPENAQFG761uYt/hRMIa0t0ZwHeuURXVs7+toToyA698tX4murHERNVrTCyAQIirJVLPrWUAyS1ZjPUmWDskxQCLIrO6/Lj4JIQilpmBBR+YYP2lBFI4opxeKTPFjqPZvii3925HY+SLCr16Lra5+PHOEnmi5u6IcN573A6SHz4ErPghGk7nm8aOuxH0nfVlXnu/bAb5vp6HvzFY6DSPL8XyLZIFlEmjs3wc7wo1apBk2QiBQAtauPZJgR/lM/PSsa/GvIy9Dgk9hJeyF3wgo+IRq0aclCrrKnCCa/l1p4LKGJlSG9eb2M1f9BjOWmzPgGd9v4xWG3AHL8zTSB1DB0ppCPiKnspe/1/5i4K1q0XWJ6xYDMkf5BCoSqlaXsdmz0EQISJsFdyP5OMTV4Qgb8kN+1lqLshGPPaHgjojEkpyRzJE2xvQg+MbHbsf2eqIrZ+uK59YT8g6ewCVl5Mk1Eydgfg9XcMPieDkOnIPHXdwwPu5UCSyj65zVhlbrnGAVR0szlePZA28jsfZPWNA1aMpCaaypXBWVsyECe0vrsKLvBbzV+QiM4CxSfDtTVOMyp7/2qNNjytqoRpsiFkfNML7DBfugAqwQceqt3B6ZdxReO118W1qLp2DQWzKudmt2P4+S9hXKLE5A8J9gAC8FD3+y0zfalJEgE/ig7bnxYMa6+1G7458AzPNYdUpc86gkM8TSYxhIdKKt+T9It63UlBTrLS+blrEvIdyDdf1v4MDY1ozlAOCe07+u/B3hGUS4EcWCSsblDU1Y5ZkLfyKlZGJboYlPKAisydooXxjnMiHcbSyRUztc12bL4zolRXQA6abFoNEBU7beqNPesk/OHGhsTwu/xno5oskIeijJWMKw2Fx9xCF11/tvQoFoKqCAAiYIyZVLitmiiz9isRgoCwQ1nzW6ahhJFTHTlbFntacLMYaP+9WUwr8+5X905feWzcCGOjE1eb5eBobs3BgEFLKIY/WbvnUDOyBQQdwUZwEf68ffh3bq4z5pYVrdSMbTnnAnSp8rQekzpdgipUlOSBYUOypmqfUsbkBauxGlFBz1Kql65TqCRPzJ8XgaQ5uQ2PECkq3LTe29NMfa4owAeCPej/QBNetQv69MV6bX4cD+0umW9UEIlk0/UR2XbnsMzS2iGTPK8HTiS2D4zR9gTecqUD6NtCA+w29FmhBf9xd1TFqhTHPfYx6CW8/5rnLtFBR3II54ypogMQYv5nQWWlQXOJOhFI8f/XFTG65YDdyRKabjRsFtV/ksUxl9BX2A89aAtbZv2pZHULvz3zrCdO2RBAJh0FQ8Bam9byL85g8QM7z/neHcYpzIMAa75wUPPCFR472+KILwG99TyFxvmsPsvhGc2CoRtJTi9Vln6+o3F6mufJ6xTlz1iV/jS5dba7YpgJ5ABRqlGL2KNZZ0j4yfGiXAq2eqAX2zBV7PBn5MdEv7Q3oY4TdvBAAIjDgSLQQhe/wb7XOSlQK1DTdg5ipzTLRMrlrGK1LnffXMuzNOgx2ssghWDhA1M6Hhnr1dfyb8I+rcr1NC2PZi6JMw8A+oca9IPu6YBXxkUNa6VPd7ae10PH2BOv8Ee7eMq11HOoqS7vUgUiwehlIs8vuxxK8ntkiWwPuHJyaXGJAtdwGYshob51NBELC052lwkgWoICk+hJAow5RauGIb0Rbdjc1DCw2DIPj5Gd+0rdOdDuLcve3waWSomtEICIA2RyXO29ehieI0/vtjZ2H97LFmF+1M3RDDKL45dgCpxrcty9q5VcddRNeNLuGEoUqPX5X57LLGMlRAnRQuY+n0kyzLsKmDm4ExuVuMH+vIJ55WAQoKRFMBBRQwIcjuWfJu6qmjLted/9kZ30LKIVnYcAll82I9+Rg2RprfwXgSIbdBK0b1f6cAxB1qy8Z4G2IP49vUUUKwRmMWrV1AeYbo0p/vi3Ti+e1P5RxceY1FzBZNz/pfBiFK6z4jg0kzYJIM/nzUJfjaxXcg6XDDFenFy3POx7Y6+5gjAmF0LksUBFQKyssnhkEohTDaiuiye9DZLVqeCRCQbloMUAH7i/XZcJKsjU87BRbVnWI7Dv+IxoWKz745TmgISKoTQYF7Tvuarl8tVvlOw8nNPZYnjXJUb0yNYaMT7Lg4mkYOIPLGd5XjLUISD1XWG8Yk/W1olzFZxwAxt1ho0G1wLxPSKNUEnU8xehcs7TtIQBXicMssgtfaH8K7nY+Z+rLDawbixYjggD4gptYdTHsnHakxMIJ5YxQY2Im7Tv+GWJqLmxMJpEOqtdk4QKC+q3fN6sSm8pkK8dNeVISZ/WPwpdXvM+FwY8M8dQwcwyLy9s24tfQnACFIsU6kpWtkDO+K0W5JvZTc3bS0eHn2uXaVTLiajiG2QrxPPCGA9L3qCVcRvGHuSBqIeyNkLfO+XcNY27wK6TzmzrJ4HHHGgzQrkTUK2SqvF+IYa0fCqB8YNdW3smjSX5DBosll0JxTp/IgdAqJLK5z5S0Lbc8XUAAAeCWCwgRCUb/ufpS3LrE+nyM6fKUAgEGHdZYt/9DeCbV/0HCYxwiTR/cWH0J02a+QblsNIDvJ87+X/sz2XIJ14fNX3IMvXfYL0/U3lNTq3PNcwyU4sU1298tsqT7syyMekGZOe/xicZ5NMU60FxebiqpKEPG/hXUnmsrI2FdUhdbyPOMSUf0fWvd+I9xjagxJS+tZAJ7ECGYNhnBFQxN44sC6maI1MNEsus7YoGXdyUJq9xLUr7sfLJ+dkCzAjALRVEABBUwSrBeKzdVHgJP2DI7EqI6gGZazaEjuXNRIKhCCay79Oc7f3YYzDFnBAD3ZY9W7tQl7BqFiTNSwpIbOhme0VX+OUvzy9K/j5TPF+trAsxyj9yXnCAvecqOkZgJRmoVI8LxXp8Zi0poUm0evF1AcGRZZjnGiz1+u9kQI3vBuAh/qxNCAOf5Jv69EN64NNUdBGG1FfOM/EN3+rHJOCHXCatf7C412j8Be68VQZBRKHVKA82lbHsa0LX8z19e4R74w9wL8qUhPnmlH1lSiIb80XV59hwNDrhJU2cYwEgvXrf8p6lffi3eb30NfkxiskuXNLlEbq4/Q/X5t9jnqeLSp2g316jb/1dSWXJ4aLFCo4Z3aVjVXN16jRZOMvlJgB1OPWW2tckvY5tPWFbFXY0HWIaWBt4M70qP83V9J8lZYO5OjGPSWYNWRqhiitSIyojExklf78vcohDpBCZDu2oXkrv+AH23De0UUt591nVJW1SqrR359yv/i5ZOj2FFt7td4qZ1uj66+KszbuVgS9AuluiNK25Ti3O4G5feoYU689sxv6X6zm/+skEtaCAzAu/TEvBzD6MiuQbxxaghjTg+GfB7ri4Jq0dQcbkBvvBlfQgSJzU8oY7ajbM7Z14GiVBKPzvg6oh5x/KFIJ/iRViR3vqQWJAQntPfjKAsXRytrKeOxzcvvQdImQx8XmW85vi5pPjRmElTaL/hHFJANFNg2k2BEXnac4jvOuOzd/PPBmlQvfnLURWjlrS0omMPWokn/fe58Yyqiew59MH1ekie93VVwthP0Lq5FRCjFTInQ7nb7IITalfIP1R9n1YwCO1fIBMtiV/lMRFw+jHrMroF2btDvnUAsCG+CuiHRWpOnLCjNrASwQ692WcmB+Htn5qn4/fnH2563IwmGPGYSCwDA6YmluIZoMiZ80JJrdhZNLo0VESUEfUUOnLO3HZpICoUp+zBHgWgqoIACJgaqbq+MkNc5rY2MNsDs4+l+PI0k+OED0lm1pJPjQQEMe4vhS3NwSL5r+lgs+Swx+vElHGYiiKRcCO/5DfjYXBR3WweKfv48FlffIQoBb86RBBSiFxwEEDx9xMWW9Z3D2TOBbaw+0nSMG2wE19Ng0r5RQrBYMil++nzDlG4haIxgFLFl94DTWNLslFzs1mkstgCVpOG6NgKC3rWpu9hM7kQ1AZ4psQsmDCT7LzMcMfoWif85UhGwFi6FsoXM5Z96AE8cfSUGnBqhzHDNmeJlUNZaYyy2I47dG5oLd3Qq0gyL4ri4oSiOmzf27089XvnbaSJFrC2aaLgUGWF4vUs7V+l+C4TBmGTJwaZjqnUhzITk1tKjUTuqWkOtCxyHKxpUyzEKRkcMRlw+XPE5e03uVk0q9Tcu0QvFRRauIwyXUIkCm8/25nO/j3U1R1mee2poNz6J3APsOqXNxnPnMgAIFkcjEMY6EVt+L9KUy0qk9frL8dx5rOU35JSC9q46iuDqOxwIGyyDFFcOaU7whLt010WJPkOnlhQkBhfIu4d3I/T2TdI5oE9DBgMAqyGZHJpsjgJRCVv1GIsX552Jv18ZwUAJBxCCpnJ9e8qY+CRACG469/s4/UAXzt/Thl5Q8AON8lWY6sRAsQrvIJhIad5ziZgX0oi9/39KFidqaMHsJm3+No2bM0Ip7LJkUqpucBwxNc7cS7POwE/OvBZbLTIHZkuDXkABAAAK/N8XWXznRum7d+WRsTQHsFwUI8M7D3qQ40mHYa5s3shi8O1DEEBZ6jf8jwqUPMWib3g/+puXILX5ZVT+xolkvw9PzPqCIkMa54o4w8IhxVF6Y8EUbPDlFhOrpbQka0y9v52ohpJYUytarr97srkOBVA3PIYrGppA4QAm6NZv++YoVp4UV9/hwN6yOvQHzJb/2RpabWeFb5jIM82p2uncjmhyGORqAAgm0yAF+uJDg8KTKqCAAiYEWRjKFK8j7RDVDwLVk0n9lMPDSCouLXIbF+1qxfl72nRlh6W1//GjrkS3pJW2CgZuB3dYT/CE3RlIBg0ePP7zeNnGjejhEy/EF24XCSsa1gTNphRhl1mTF1v5AFJ7X8/YHyXEJGBurpqH+KoHEF//F8sLle9DyG88nmGR1zyvW86+QfxDa3ljzf0o+O0FJ9uOQy6vt2iiCEn3hMb1LnYWDWWEKR2zYQNq3LTK6HD7DOUyZMIxnOMJC2FEjBvED+4zFV9Sp7kfhtguVjGayp4qQ/nL2TSO+utyRftNJb53ydeVLH5aguLhYy/UlVMt20Q4UnrShhKrV8t+fCnpOvqLgbRL767IWFjYsFwcRV3rbVulIEg6XNhhiA31Wvtf8Hbno+BAMZzHxuu1WWdjy9RKLDqBAKB4O6EPim5lbZfZPlIEk4qCk+arsNe6jOp+Ko6XSSd0rmAUQMIhblI9KQ6MzsWWgtVktMRAA6iFe6UVfEONyt8i0aQnPB2JUTxxzMVomqK6lBmJayIImLblEYXA2ls2A2XRhC7OiHSRMN6nSxDGWizUFsgYg0Q+9v4C87lRVwBXfPoeUw0tWLuMRIbS2vm0fP8bliQTAKyrPdrURwEFmGF4R0JSrBn+o72lMinBAM0UcfDc6uSnIfQ64VvPAoTi5vKpmqQRBCAEC0+wHkOwfztKR3hcuKsVW6pnW7p6j29cBO/OPl75vXbKAnzhNhZdFQSmtV1npUYQcmcgf0wdqetFNjlYkWukNcbodibfoc6ouJYwdq7GWayl5HFkUiZoYec6p5XCmHQEcad4JOJSlTuHt8NmAR/tWbGAAgqYOKiaI8UO3ZW9OKm5B4QaMhkZCQLpfzfHwylQZTH76s0sbrxOWnKIGrIwvy2BADYZAi85L3GwD/Qa7Num/L2w/jQ8dvQV1gsrkYghyoMko/oTFuCHGmHMRGW1AWMFfZkRbWwqo/kxNGSfoZ3y1sWGklpBRFPa4tqGHG7TsZWazChRA1FHCTFt3o0WTU1SDCd5vHddejJu/1r+aY+dyVHbc2XNaowV471t8urN2yMuG6YAgDCmJyZTrAP80H6E3/yBkt0uV1ALAo8IBMRG8tLGybrjrO9YlpGzHA35AthTJ/4tu4vd8g0WbUVmTfKt514FITaMxLanUdqxwnQ+Hw06Twj+dfbRCsmVC+QYB3xkttpnli5rejtx1J7tOY3tmv9RybVRTxB3XXomwj4CZ2JYV87YUjKTZRsACAJqdv5b+bnVX4yHj7kKT18gZ5eUWpQeGycRRxvmqj29OO8CtX/CoKG2Eie09uKYzgF4NG6IAMWQV3VLEN/ZXEVpjfaXASr3vwEmHUNPeQIrjoqjqvFVuMLavqwpP0cqU9BT7Tedw/uiMXh9sFS14Oty+03dv6oh9AVCLLLA6ftrqJyj/M1KLhb/OuISqV8Bc/tG4EumEdXEirMK6Pqnuecg7PTipTnnZ7+eAgowvvbyPM6Pz93pvweSXCaRJrqvlz+EgfVNAer0D4wxBP8mVADl/fBwPBy9PoQtrGvKh3iUG9zsMylXoQyBYOmxBGskAyBqk07VoZEL29w+/OHkTyi/d0gW53agmiysciZVnrFel6k0//HD+8UhCvrnEnO4sXjX/yG20ZwJ9KZzv6/7vWq+aE39n1lHm8rO7RuGL5mCM53b2mVn+cRq9gjucDdWzCvHEx9j8NwxC3Jqt4APHgWiqYACCpgg5E2W/YJCGQHV4RgoDJsHiyxzTcXmjFhxN0HKaU30KH/K65HJdJfojr/nH8Du0bX4u3er7XgdiVHRdURpMvNUyaTjcBKC6LJfIdHwtGkDdSXCuLpkEcYkXuORBZ9Uzj15gmgJc2n7BgDA1PgYOoMakoAQvDXzDHUshr4pCNqKagAAvWX6jj1hezc9mYSJsAbNmXS/OOOzAcGAL7OrlylroNFaQhq9TEBtnVaJ5lq1zP5qM7llBznTSKB/O7R3pai/IWMIZi3iGbJKpVrfR+N6c/wkcHHzsSzIpMnLBAIo1ntGaLMcyZCFsoQLlhzCropaRBfeDq57M4jA4YuaLGoUetck33CjdSMabJpVi6FiImZByiEehKJ5TlpYtNlU7y/mUR7NLQhnxGMdeNQZM8dO0RJcjy+4VDc+IxjK6QgKSghen32O9ZwE4K2md/AwEhhzyBsAisbSOnAyLwUAhKA2FIVD0H8lRKDo1GTw0889FM74IN6btQD7LG6hdm4dLCJg+SS8oTa8d1ofDkxJg+Xi8Iy1KmV0QYVzfEe7HOL7WLrr9JzKy4HtN1TOwCCrvrO7AmWGkgSPHPsp/bUY4pSwnD5e1WNHfxxvzjwTAOCT3K9jmhTYRYkUzt/bbsg8aX7Kq0trcfWVv0R8wwsF7XgBBYwX0scTefdWRN69VfwhfW5CfPSgddsnEXzVY6Kyb0uwHNrvnOGTKOlYpcQKFTJICO5wN971+/GfI/WWtUVRitOae/DjvvVYuuc3gNSDNkulGeIN+duVLJ65LLeZZbAIaGRSiEO1cLr1nO9mrpRWZRKZaErYJGIRwj2ILr0bqT1v6MYo47Mfvxen7W9F7WhY1x4gWrdqIZ/q9Jvj3RXHUzh/b4firmjVlxZa17m/laiKlhanqgQSqKhAeecUBild6IvCrH04o0A0FVBAARNDxtTcRveJzKQNBcH3Lrg5a5eqBQ9RtD12S81Xrv4Uvnu9uiiFPE786Ix/YU9ZZu2jO9qHknbJ6iOH2B1OAEKoHemW900ESwgUA0wC3/qhA5+69kK8OkfNKrVwrj6I9JREGIun693S9pbVg9dYcd112teVcxTAy3POw21fY7FvWoYFV+YDJdaDEuDl2qvw/JTPK0UIl8DPT7dP16uFf7gRewLzsb5O3dgZtswmLVV7sBoAMOrWx0DwSdq1fCATgcG+BtM7SJT/Keo2/MG2DZaL46X5NhtmCkQ1sV36vWaSjetpsKyaySnL6JJo3bU2LoF6D40ZBt1jHbrfMkkbd4l9bpWCha890vr91WZxFAijM5PPJZOLS7o/BBTlTe/iZ588Eo9cnoNYYTlnWN8YIVmdvb0sbZi6BxB1efHsOSx+eC2LuFNPcGa3njL2o85HADCWCuNpZMqWaCRgVfhHmuAQ7AnQ8uZFeOjEc/Hzr1jMX5phy+5x6kGjRSPgHVMD4uaKm864HtN/cRzcT2yxvNuDVNx0DEn/y5u75qIKhXAdLALE7R4xjWlg82NINS8DmxgFqN7SzKX5HgFRaz8gxShLGWLIaYlPCop9JdNNWTFNyOKKV0ABmfHRc7u89ezr8cfjPyf9kr65dBQ0MXrI7kZIYPHnmdcj5RQVEr1ur8mttqRrLZ4/h8FbpxCsCRpJbhUEFGMsi3+dYE6WAQBM1zqkOYmE4WIo6rNXWGZbj7566U3K3w7JSrtbUhYmx5nBb/McgjdOJXhyxjGW1vI//ooXwlgXtO9qVHJDqwhbKNFyeIgcwyLKWMvTIx6NrGfyYFDHp7Xib/CoMuUzxRoSS3c5xPpwAYcdCkRTAQUUMCEo25kMMU+s/LWXHGcRsyPHxVXbns6tzAJjHg8GSsa3FCkEiC3RpLZ726gmHop0HX86/nO495T/zdKLfvEVpHgCmbC+9mjEJIUVJQwoYdBSm7lOYHA3pm96SHes2zMFMYfeoqnfbyOEGZonXAKLKy9Ei5T+lhIYNndmC6dHF3wct551HVpkqzXFjYxDza5noGyG8xRRrbf91r9kRJtEQvNxQywjbaNal6rl007Qnb4UY4hvMGfEswIlBIksqeSN5QFgS8ALSghKo3EEY0k4UhGsrlVNxsVYVerVP3DSF/HTLxVhzE/A8Gl0BKvFgJ/TVertvs8y+McFqsbw59Mfws0zHgAAMCa/zNy/G0+kG7tq/VhyvL1YEZG0ltoMPXIPcgw1NeOeWJZIQVGDPZtzHktOEHj85ywW3eUW85AlVahxj8tyWxzJkKGu9L+s3Te5hKlgubgpI9ovzlbJYIZycIf1BGMmWF0dAFMcEvlRa7+90rZlptpRlxf8wB5N/BND61RklgRDz7p7SkUXNrc2s6c0xyb6diK5/VkU9Zqf9z+LgnjnCL1W/Xmk8Fck8N6AnvQ16tF/eP4PcOMFNyE7PnpkQQF5Yrwmqv+F2FExG+/Wi8qaXOW3gwUt9TCocT+WEfcQPPUxFpxN4GlAM08x1mQ/I3BwSG6AKZpJmQCwmth6VitKv19VXpW1LNGdHfX4sPwYgocusw9M7ljmBGvQBwkMwb8uYhE1zGMbasS4dG1V5oQxEbcLF+5qxZw+fUw/V7TPLBNI4Mf08SJvPVLOskvwbFEQu6eJ/Wizzv1tjt7FbsShylcJp16pcGpTN05q6dFZ1i8u1WSl1ZFWhTn7cEaBaCqggAImhhxiNMnmt7J89uVbWPz9ciZjnUxgFJcnzQJzENYaklXDrXbaxHF462TxeuR4Ie/Un45VmrhGxjpWGMwxCKRxg24HISrGp/GMdYDl4uOI9pK5f7W8uWVdjCZKwTEO7NDEVdHCE+4CFUShhKTyXZoyUEu6HwQPH3MVXp95JoRUFcpal2TcNHDaOAeEIOxWyaIoAGPQbzsQEPzkYx+X2sz8BGp2PqMMaVGJHxX7X8MZB7pxSnMfAODeU7+CL90qB//mdReYdLixb6oosDniwyjpWGlqf9M8Bu8erwqvmwK7sccnBjnPbJ0IfOGrH8t4PhvWezx44MQv4j+zVYs+efhO2TpKGoOQ9ODtqkvgSIrX6khHQfjMgn3OIAT1G36v/HTpYiTZIJdwRPIexca90mhjpB6XXB6dQMzhxGNHf1x3fkv1LJSHYzixVXwHFBdHi3ht+Y9bsiqS4kqxKXVzVNyzCZF3fmdf0/J90ceLk6/ZGVd3RESq6w21aEcg/SvW9A43oqxFG2MOGHKwePhM/XyaBvAMUkrsPSu4Ir225wooYOIguv8+uviAiSaFyBetdZ+6SJQjrGSTPmOmUOUvWaFgSH6gOZtmKU5s6UEbk8y4ZvoHd9uP1UD0D0kiz7rAZUqbf/04i+ULPLpyfJrBEEdwzS0sGpbNRPWd1i5yxmdx95lfwpdvUeWZdsk7m/ApgBJ4ON50l6bs+CfGPNaxnrg21ZLLkRzV3YcVPh82zDUr1gbd+mt5pVwNk9HH6i1SKyJxVI/plRlJloV2FV02V0oKVOCZDmsUiKYCCihggjBuKczQWiARLgHOIcZqMmcpEX8/fAWD1gzZx6slf39/KpKDFk1dhYqlrFfZy8pt5udKIdfyhDtR3vyezVlrvCxtvodceqLJmKJcdquS7ykR0qjY/wayQ+9CY7c25y0qKhXEFn/3iYDy2+7ZeKV4KiaCiCsBADDxHDICaqULo6RBtH/qz70++xw8fNxnAIjukYFBNcbCjYiiRVAzyhld/755zWnoLgMeOvP47OMDsG14GVrCO0AJQcjjyV4BgCfSpXMlcoW7decpYcCzFvfVdIjCN9JkOmYHloubtJeydQoAeEZbTJrgEV4MOjaQzo0cpYRgSd3JEKwClZqGRtDknw2SzxuZReB8Z74Yd4PE9dfhG96Pqr0va7vOAAuTJqPrpoFYN8Jo8dToEjcLP/gOC4GI2fd0/RGC05p7UGUIRju+fV3mm0QE4wbLvvybs85E2KX/VpUhSd9+yCeSsY6hJmU27THEkiNUtYYwZjl6ac55eKtmfsYxW0LTDKHWm8YCCpgUfIR3uv6BnSqxblrvD9V90bvgytl2U3ZGxJTinqrMLtlCRB8rVCtH/PW8GXjtzASaivVznxw30rJLALXbn1J+l7av0MX9G3Q48MXL78Irs20srCWs/uvF+FWPB2lHtslff14gDDipzmev/BXukJKwmOVUPR4/vwIA0FFUqjseX/NvjbzbDTs4owMYDlirWLQBy59zW8eitAcFZyUHFXDYoUA0FVBAAROCotXOyXWOYMamP2doTKyx7DgGt34zu6tR0ViPsljnsuQU9TeAcLLmJAchiIwv4CCFVXYjtb+pW7UZPWRtqHX7xYaU8MH+7YrFlL7VDIPR9qZsgu2flxX+eZI54KO+XbHhMa/YSpphdRZN3tEW5W9toHXtGPeUzgQARGwypuQKPYFJ8XbJSgyzIdvyMraAR4SOKr91RJPAI+Vk8cPvOLBwfj0A4ImLrN/R4q51AID2gdXYMPi2fjy5vEY6LyNZiB4HNZhjHSFVBiJw5j4ElUCq2fsSqMGCa1Q635YSXeG4oVNBBRZsj99+TBnAJsdsyUkK5LSh+92nGfz8PGuWWkiKxwO9zabGHcmxLM8mn02TddnHL2aQcgDVO58GEVTrrNcCAVz3jVqMBHPwywNsy1BLZiuz2dOBctFVdtibv9DeEazGNdd8UndMsSqQfm+aF8XCU/owIlCEGAbPn8PgH5fKlgZqHVnD31AhWjympWt8bMEn8FS9PmZdJli53dIs5wsoIB/YzsUH26XuMNxXVza9gzrJJd+RMK+x7Xau+JMJg0FZ9i+cgst2LyP28dxibgeWnMBIMpvam3e0WZf1FgCCPRuVv90x1d2MEdIo7tYrPkPuQNawCaCqgtYZNye5UIvZu2fHnCpRxaZjYFNh23biThuaIMd4dkYXbRk7NG6DAJAi45H5DsMPogATCkRTAQUUMEHIFjI5uM6ZzkxsoRAIMCDtbMZsjSrG30e+BIGWUMsEp4YoIgKPqVv/rgivdtsk2dqEcnHU7H5OCeqdHePdWOnrLZ2T2WpFHs7u6Q68cgbBQ0efDa0xNiNo3Z6sx/7ogo/j+gtuxoDTzhxc20Tuz/XPtc/imnl32Jy1vz/9PlVINlsH2cORCoNwSZzZ2IGLdzRL8WdsPwJbEKj31a2xNEkOXIxo8/etK2lr5yAMRg7cgmiL2JZdPAYZ1Pa8OEghMQWRffeCSeRgkWaBqdseM7Vp/cse649g0FRmeH9MinYrl0f12rLaSJqEeINFk03UsIUnMfifWxxg+QQcCXUOEAjBQLFEWub0Xls/hwTD4sHaWXjwU4xFWet2n51zOW75BovORMC61HinD+k6BAborkwox14+m0GPEhdLbTwwILqZ3H/yl3HtRbcgxWg2ceOI/WIis3PFR9g6pYBxgnMiOXgBmA25ZWKcMA7TV9SZHMXUbf/QHWsnFdK5Q2lVmHm+IKCTdA81awaXAAHgG9UrMYjh/1zbzYUQZ0zWp4CQEgmcYP/OvHrMNBb1X6sz1r9kmV8bj5AASEpKxCVltYY6h+lLXcCEkXt00gIKKKAAK2SwaDIur5NONAF4pboee85vx856BthjOcD8G5aG5UyMKIe8I802he1gjsRiB5aLg2Gl2DQ5ZLgDKCCwADill9TwWWDHkgBaDEWzbYJte7DEDVefBmfJJqBbLmd4xgR49nwW6c1eUBK1aAG2t4JnWLQWT0GW3FBZR6rj4LSZ1DJoAI3N7fCXIcxrYu3kaO3iSanCH0spHMLEFN3E4kGkBi/S9Cw1zpmXc3thVT1O06rJur3VlNxeNuT3rVU2vqZrk9G4OGUTtH+6/a9IH3s8fm4zDsYiWLXd+MWNx+QJusbYbhlstCzqGjYPUuWnLmIw5vQD2+ybSDMstvuLEbXIMii6t/hNlXiuGjuHf4u5Q6Kl6Tqv1zTOf1zKIJ2jtGiV+MEa+s2e/LxTrBMdwWrU60uaEHZlfhvducTdythDAQXYwOSpTZAauBS+qDkm3kHBYWzIwWrnXUoxjCCuaGjCLiYH5dFEketnnMdcv6r2mJy6YPi0ZQmr9Tsv5Fk/1nY9GNcAysP/AVtWm73CBMaX6TVUYi1qVnICii+f9XXEyo9A5f7X9Wu0YRz3fInBCU0UsEuMSik21RXj4r0DaC52ASM25Qr4wFEgmgoooIAJwqwxj4x1wecOKktMa7V4bp/baGlg3BDlt+pRiOnft87Rt/Pjb7JAvArB5i2IF1uPV5sRxK5/7WbXzgRYV14pnl1jdvvnZiFZ2gZExYJhl7jBi7GZrUEoISDUbDOV7PuEFPuoxaqapgH5v/ykVc5djPTOzeg+zQ9XOYHT4JZvbo9kIM2y2oxkH1DOwmIWrZldOyQXNyZ9bIiLdzSDocCL1XrXLao1sc/htr9avAbfHroCET6OoGHMseX/gO/8b6ttpyqQ7L8M7n0AKnS9ItN9tIonkVWLOsmuIf7hRqBY37RWQNUPTv+7LdaD/gyU5LStj2DgAim2T2bvMROyfxuG85SivOldoET8KROaYYmz4TLE7jDCP7gXaWE+vnzai2ApA7SdBgB461QGQsqLym32w8iFRLZ7xnGGxfeqq2C29aJYdKL0HVsS+Yb2bS0zzVAz3dkVsG7ll19k0BeydsmxIkuFrLflMN65F3D4IbNh50cblKK1Cqjv11sVHgoqV7GTzPIcnIlhZB0Rpbjyk7+1duWWO6DQKFrVeobCmn8njn2ldRnPU64IPJc5zMF4QKiAcoNboKZX03VrEwD98RNufG5tEiMZLdX19XfWM9hZD5Q/oRuFrsyG+lJ86VYWjq0uBDBJiUIKmHQUXOcKKKCACcFKsN+57SlE3/mx8nv/VIJv/IDFFkNAZFNNwwE7K6LGkmmZWkF7FUFLlRvlrUtMcQPkpcoTslOVTBziRjWzNUljjQ9dFbLAQvHi3Avxl2M/jfcr6y1r3X/il3Cg2LyxpiBZCILsVi0yqve8aN8Mw0KImdVGtkIdpegI6gkXOX6B3HN65HRwsXo4myvt+x0HZLdCs5CYw0Zcs1E20o5GyK5kcjBUp0DBar4HbXBSXhpLxJV92X2pdBUuP/IGJGkaKanelkoxPTGVzOX9A3IQc4LU0PkgKaMliv4C9JnVCBghjSrD895UNR/p9jV4oOJhy3HRHNWzE4mDo9SVrjsj6aOxHCptW647xQgcFBc5A4mVsV9kH785YDYQHNhhIk5eOpvBo5cw2GrFHdt+NwJK25ZjxDGGQeeoTZk87q/t528eAE/IuNzUtFA3e9nb2e32YsQPvHZ6tm9C39aOmQxGvJljemgv2xjUv4ACJhWH2iDuMDfA6y851IybXuFDs3zvjFX2UiuCmmHNbWnntUm4zJfPJFgzXz+XWa0/cYmjkWPYGZFf2hotKDJdiPaMUTmb6TVkBHlcBHumM/jVl/QxO/NVdJrHJfZumRilgMMKhdW3gAIKmCCkjTlh4JXi2MiCvbxI8/GpiPiyu9YZUdX4ik2P2ReXTAEOs3U9XjnOqklj1ji7WmnWgTdnnWUrJC2tOwnfv+AmaYC5j5DmWLa0dSnc0V444vY2yPG1z+TcLwhFo0H7ptwfQpDctRiUDyDedh1IcnwxfWy7lsQuowjlTIZQ1LPJUNqgSePEQN69o+05C0POxAgq9r9pX4BhMRgI4LGLGfz2sgr7chaIMwz+95Kf4c4zvmkYbhYSjYj2fjJqdz1jymAn1+BDfQCAfyy4El8/419YWrFdKfP5K+7Btxd8zND35MHIXcljMlk0WUFTt7hno8lKy44XsyZBqI5qygR3pBtlLYsylpm25W9IOwgWnsTkRLqMD9bXkRGKBp7qf9uWz3NEUvlcnl+UZfGdGx1oqbEuO1l3TXf/La83i9VjAQVooMyb0czWJQV8MCBcInuZrCWsLZPsa0tkV5aQCeG9dyO8727l9/PnsXjwquwZaTnGvmcA2BPILFd4h/dn7SM77FVvxnFtnCs6TK0vMyTm0CniJjLXFubpDwsKRFMBBRQwMSiLBYErPoRpmx/G/Sd9GS/PPhe73aIJb6z924g2/9BUtaLpbf0B015Zr6cxxl1h0zGLjcMEticTXLvWHSH2vcdfPOHGIu/dhsh7t5vugQg1WCTNtNpbwLIe1Htt3wQFuKSayth4VmWRMvSu3pP4yifgCbVlKzY+UN1/OgT6GjJWXTSlDeFXr8U7Pp/hUqxaUwswvFG4VWmq2OqnAFC8dzKDEX/+HuuDvhJNKuDcXfCITqgTLIOHAgC4JDxjHRAYFkNF+oYjLh9iDqe2Z3tM4Lkps4hsUWYgZ6ybHr8e13YAQFbyhQAo6ttmqqzbihi05jW7ntW3YWvOabSly/6g/3GRF3d/WQw4n//sl92R0A5lrUtMx5qrxE3TsnprzbsRkf23I7L/9nGOzQxi+L+AAg4Gos0/RKL1yx/0MA5D2KeMOFhwJEWrdXlOJZRD3frfZ6mV2yi9eSQBkZu992oGD1/BqAd0592A4Jb+tpmlLIb2i/9h8eJZBDGnSkrpPAQyTHhjHhbe0daMw/73BQx+/KkTM5bJdMuMpzoqWVx9hwNtvmDmwUnIyQpae78oQHkpQU26QGUczig8nQIKKGBCIKaNL8WgrwSPHvNJdbMoeCAka0x1XZE+VOx/Q9daJpS2vw8AWFJ3EgBghNVk8wIwZdtjYJMhQy3jQi/1lHEzmds2RUhVgvJeMPvFWDC7ZjC4+g4HutyZM7SZYR4LjY+AxofBcnEEe7cYRkfHrYRXN7jWMXDUUANiwYfOOFkjNAHa+ATGzXNGUK0f/8HdBiqxaiwsSbIJNJ0VooC0tajCsG1X//YPWQer2V6vltHWlC2GAEyCIs6uAf21OuMjFmWNxIz6PHyjzSjusA5mq9wH0+2c/C0Fo7gu6juz+l5z14hmLudIhnVlWBsyNVdYv2OZNOTZD2cq+krpj7CJ/UqWUuPoAMhIuhX1bjGR/wNFTlx9hwPrps3I0q54nyhXAsqVZO5bm7nIjizVNozxUpAFeqqA3CAka1TCQEHh/RG/axWH4o44UmFM3/gnVXaZSL+G6c4z1mE4b7TqMa9XDbMZLDuO0bVnvVbZ+jWbjnRWErx4LgsIPPikaCW0z+3Cr+adY9OGil1TAln1f6+fzuBAlX1sJ6twENT0R/b7bnt+HKJEuu9cJHo+DUfH5MekKmDyUCCaCiiggInBQB5oN1nCcGfW6iyXwM4ZOcRj0eCNmWfh4x//FcZYvW+7KzEMryH2klVknfEi0L/D0JQLkcZfgAyaYwzZWWa4w51ygZyHJPerWElQjUVTtkHbbBRzMS8HgPfmzRGFJqkZruc0pIZPh6OlEp6w+nyp4S9zyveDqeekSGi877QCp1VZ/U+KZ89j8OxZPl1d+/qGzDpyHUpx7xcZfOE21fKIWraWjwhsMYIMZN3UrX9Xjgf7ttpegBKjwnCeyfGdmEwMB8XxcnIMK0ljKwfHV5HHfdOQ3rnAO9qSe9sZcGCK+H+TVdDTvF7//KyMKFcCLrxAfK45km+T9zWOtyWaR1XrZ1+z8+l8qxRQQAEHEQQUW2eJH1+vw5HzfDRRsHxSM59MxsdvkF+UZTebhDBBYj8LylsXI9Z6AyIHbsm9UsbYhFkr63599eKf4Mdn32Cqm9sdz3QvcrlPVFE0BAZ2ANSB9OhpyDWTcgEfDApZ5woooIAJwqB11ixqfKgHdesfRPtpN2Wsf+8XGDh4AI05LhiEgGfE6ctMYOS4sI9jbSruXo+xCTaWS7a7gwl5UbYeRw4QXEj2fQqEfyFjMUc8BPiA3yOOYhP1M/mCwY++xWJGPwVWq91YEpcWt/qVMxkIKR/QBE3sGoKW4ik2vRE44iPYOI/BV5YBy4tLRX0fITqWa8Kpja1gQ2CCEDiTIShEHzW6YKl1xWxAHvW8YrA0fjeq8eLf5zvQVUGxY58bbELAO/WnA4RgUc2ROdS2tla0Q7ag+bKxnpFINLZihx0zGXz7RgLHC+4c5iXxd0/AIIYR+/dGfm52I/AP7UXKV2VzNjP9engi8/PyRLpBeQ6EVe+hepUEqXNZzPf2AL31OfdY2LIUkB0fpm/oUINiyfEE645g4XnOgdJD2LN+hpvgM5KqO0wW8rkrRHV1JmliIQIPCB5QQVTIDEuKmb2Bcts6Zrf+8YKi31+Gfr91xk+xhPGAemTit0C1qmK4BGA0KCzgsETBoqmAAgqYGEyuLvqlhqFZXB0oBc8SJF3ZlyH/4B44Y4PSLzOxlWl8tr81mFyrG5VUEQNsG65PzoxmOBPo346ssEmhm3kc5u5LW5fmUMvGbUoxCbeu7xvaCwD4D9J4wph6VvXPs+0/XwyUEGyax+iat4bdvdPX4nobsWbKMbZtTGt4FF3JS/Dl2xwYjnRnaFd+xofB5sT2vWFM5xkuqSknWStmN5/Le0hpB8GiExll/hAYFm/NPBO8RB5oW6xoeie3RrNNCRZHCKXK9bljfajc/7quBMPFUdSzCTV7ns/YYshv9/aZe/3O91j85OJq6ZfWdVVbVtOeIGXRs/tuMn1PRCYg8xX31Tbt4puN+IFXjjNnxMyn7fHDaHmgbjOFSgae0myudpM0jAI+wii8QDoQgqjXOM8cfApXzfJqOmMunKPsQfgU6tfdb18gSzu8NN+maeYsmRlGkNFys9tbhG997Da8XHvE+JrP59U1XGvuS4kabsG+c4o/fpLBT78y3vtUwOGKAtFUQAEFTAjEuFHPY+EiBr/vrGlpKa9uNhn9giRERE0+yeBaNHGoY3WHu2xLOQwZ76Y2PJbFSkJD6whp01kr6wgtrZcITgMAxEuzB+DVxmgq7t1sajHfYLqm5y/BkYqYXLGIiZRUW9FjYoJ7fpZEauGqvS8pf/O9+7LWTA+fh/Ce34ChxHLIjJVu1c5aJT2xuEC56A3zSX5W1LvZ8l08VPCE2kB5PUlg1i7n957k8164x/RuvwRAWdsyuGIDuTVgiuVhxkiQIOEchxiW4TkmgwYrvGykdB5EfUXLQpR0rDK1950bHfj3ieasR2xyTFduvMiYtc8mZkoBBRx8FOzfTDhErnJWmEwLYuOTZaRkKYJdIZtXQZC22aN8pridVpXVi/FEui3Oq+W6ApUTyGyqV3VaJXgARFLJJLXpnrWkyDCU8YRabfpVS476xeqrj2awf2qG65hcMbGAQ4QC0VRAAQVMDKaArflu/tTyRYag1zYdmo6E9/0C3O7P2pzPx/on966r97wI31CjZTF3tE/3WwzebQhPaysVZRcYxPZUEiMZqLUvbCP4yUeDPZuy9pe5PbMNBgAQgUPdpj/n1/YkIc2KY+osKjadMwXl5ETLGT4xBb7RFhgN76PLfoXo0ruNrVj0an7PXpl9jvmMhUA4devfMbXhUYs2rWBHpOYihVlbf6jsIiOVYkBAEZQtWLLEPDoY8h6biiL0j69OrBHDwAa94vsQYSy0ppSO270hpw1O1g2YWWjPVtbyLSRZIiJMcDdmDMYtj8E45wHA9K2PZGktn3uSWx2tVQNH5fc5n3tbQAFZ8EG9QgVeKyMY6bk4Ij15180Wy9MhyW+8qWBu8qXl0UNJymWy5BdEi3OGi4Hwk6tccqQi2kHAeCdu/xqLH32LNR0v4L8HBaKpgAIKmCDsFwgat45oZFe/tP19nVWJdXELjbzghRJyzrh5zt6tBbJLdIyQhjMxbF9eYzmUuU2Sh1WBtbtgPq5iRqIlU93pmx7KPC6LsWRueDKtzezv2ZiPxa8/z+DXZ16UtR6TdiHaej0S3V/QF5N4wc0uN4SxLrgsNtIZx0OBvy/4JK646j7xt71dP5zJENhcA3HnIZyarDtsq4rPY6zmFH2xQ5QlMBdMPNgo8Oz8j+HeU/4X27w+i/LU8s/JQ46WREZZPO9bb7fxmSxXljwtojK1k6WqbYbMDP1rs0D9KHwNHuGuxO5oSV4je+rIy/DarLPzqlPARxGFzbEZH9w9YQV5BDSHeSm/cbJUdFvmdXNRPhN1vvOtvnx6VMy0zKZykanz7ElyyXZnJeio4Vc2ek4Pb6jFdKy5liDsy/HeFD63DyUKRFMBBRQwIRizq+myzoV6szdgCBZIjJY/2RvIs7ymswkjU99Gwsu6rCmDmWQxcMs3RLJEfzK/EeRWwK4aBcvFwUfmgQou8D3HSUOw0dh98FyEDlvnMIi6cosWKcRnAFSfKYwmI/CONOEXp38Tn7/iHhR3rcurfwIKEJLVHdQauTw08Ya75fTLpuDgGZoRDNaH0k/B4cljjBlwkDS1pkyGpm4ykLkAeIbFqqnHme+VxXDzccGyFv4nQsioZTO5VI7rLttMr7bXayL2bUZhdzwj9GVK2t/Pt4p9PwToFUrwa+4a/YYo07Ck9+K5+R/D3479VPaxFPDRhO1ad5AXwQ/BRvvQR2ZSsXW22Bs3bjcyQJ3P9DebFWSLJoK8rmo8ayE1E2WJns8ivOdeeOX1Pqd2ciw2NANUcIJtqzZV0l2pob2c7gJVS7I66yZ9ezmtt7bhDz8EH8ZHGAWiqYACCpggJmL1YDalzarltl0I5YDFdhp9e3eT3JGP9U6WkxIzU9yzUXfYkY4iuf0dtFUTbJ0jT9Gqll78w6hLyn8TayaGrF0fKR9EZN89oPFKy3ZsL91qSLlamx1M5BxInaK0YyXSrAMRlw8EFIRP2ZS1eu/M7U0KDPfQP5Q9lpSCaDFSQ+fCsfkow5DExlSrrRxJB3OB3MeSJxw2mtw/fpLBrZ+fKfYeEgNSs31FObaqnT3GTwzJrQhcUFM/5+oZERjYYT6Ykd8+2EK3/ffiyhhLRA+rmHUl3Rts+sohGIoEXiJ2+TwmmUJcpwLGhUP92hxmypzDDfcd/w1883NXSGQQwMen5d2GUS6SfzoEs0UTASyszY0N5j0EeMJaMknujwEwOYGyHfEh/YFkAJF9vwQT8SHzBVify9muazLXpsK38KFBgWgqoIACJgjjQpvfYmJPDOXavXmxs+4nl5UpOxESa70OkQM364pnbCvbuISUqWx8rX2WEXF4VAk0nR0GYk62aJDve87kj3WB/LJYGTeOkyN45Cdz5Ehs5j20XCtMVELKh9g1WwAl+68AiXv15yXBPNi7dQJ9TS7kzX9nUAzyHyZW7hAUq49m0FIqvtQ0WoPwnt+AHQwq5/XFMzwji1PRlu8i2vzDnMcca/keYu3fsGzQnszIfG8JrMjEHCwpbRuchJ2QTfUpmuxI3tGWjNWcFmnDbZFJrW54pq/POhuvzjoHb1Xkv8ksoIDcUNjl5o5Dx8YlEkeim7tQ+c2FTrYtm/0J6sf90tzzAQBjrHN8g8sDLJeY1FfM2NS0hsfty47jcdlIhjnU1HSWw77BylWvgMMfBaKpgAIKmBhMwcCzIzlwsbYBY4OZu7MLemxyUTPY/CgxciYm+PDxetCU0brHfO0qlUKsS0woKC9FZ8A4Btui1uPK8rhsR5cluHgOLcD0bPKqmw/yd9/JV/ix7yq79s8emWLS2HRnp1HNq32be2EoPuJmpMOyVVs+feeHp+dfjNvP+g72u1zmk9LzYfjMGftyG5Zaiki/hcR0CMmaHEcKUK4YfHSepg3tyXwspCaTtJzoQ8mXuBLhG23K2nLW6xzHXJ10uPDIsVchZRXwXWw07zYLKMAaBuvfwj7YAvJs+oEPYUJ4c9ZZuPxTDyDFMLC8JtsQCR/klY/Xy8DcRq7Si2UJqw8jj/3CL7/I4N0T9eWzW5AXcLigQDQVUEABk4J8tA2pwYsQ3vMbqaLR6iLbxsZu2jJudieHWLLuKgeLnHG43qh17dpSrcZuPft63Hn6N3JIa5ufJYV9axOX1ogNcXHQYHHp2Ta3lA9If+T73uRmKZWfBVgO/Zg6yFR2fEIw1fx179UMfn6+OZX9wYLAsGionKv0/7sTv4AVtUcbSuVpIWecI6zqTnjayNxAyi8SxQJrkSUuR9LSGlniT+VpwUdtgm3bw66hLNXGi8KGo4CPGj6Er/yh5+AoVk45Ds1FtXirsj5rafO8lttEqdazUDbmHW80h+4nuymr5DoA4sq6pFHA5KxkzHB+HNe1YyaDxy9lVYUWUDAq/BChQDQVUEABE8JE41vkvsBLZyViRY0HYlzordvLaYOvVM3V4iMHjMtLLPvSPeIpwsaao8YxEDsNo97yiua7IwUwdevfMw9BcXM0BkI4hFJDlk18rO1bSPR8GjRpPpefFm18Qln+yER2Gn/rrQ+JkcDMUp8AaJjNYMQ7ObEiMvZtg8V1p+C+Ez6fpZTddeVGDOdje5Y7jDdTFL8Ehzd72XxIayk7kvL7YGvYc27OouCkkkQTthUsoIDckIuiqQAJH9w9GnP78d0Lf4Tk0M6D3pfVVbJcZkvbjBjHRFW7/SnDEdniODfIctmnP34vvnnqNQCA3hIHhgPAkwtOsa1H1K5sLPcn0fK28Ml96FAgmgoooIBJxgRXgqzVJbcdY4whmwZUV7GJr1D2TVgtpJO3IqqWQHaEQKa+jGRJ5r7ahBR2g8ejvWsMg7DuO8QdAQD495yj9HFXcgiQbkdSTsKjyoDM94NypUiPnpbXPQVysJSiUmwHLrdMeNl7zo0csi6Sjeiz+a18c9n7OJg42LEaCCZOoNtpiq17mzz4hzMHh5/wt5WnhZMcIH1i/Wa6R7k0XKCXCjgIMFlGFt4zO3yQS8Z4LItymq8IRf5Wwfl2kjvcsf7sfVpBN6dTJBxuJCWLppSDwXXfd2BL9bQMLVq895aK3Qz3q0Ag/dfCwma7gAIKKGAcmDQlX5YGiH7TSwzHlbXb1krmUK9ohhuTh5FFthZzguF+qBtD4/8i0qC4FlFUx/qhs7Wg1uUT0RNx+WeOQrB7D8qH+5AXJuNRWG3gaSYi6yA9/yzWGXzkCCR6PwH3AQ+Apdkay96PiQvKTnaaCcvMY7YlEWzjUYwHebRhIjnybJtS20oEFD1l4t8RZvJ1cDkTV9QsjGdyzzDXz29TRTLYLqrjya9FLeId3wDrP4Cy1HLk/75kL59JX17Y9hdwMPCBBSUuvNCHEBksujNYxx90C9KDDEKNMkJ2jJ9EO9jCbwGHAwoWTQUUUMAkI8/FI8+sc1QhlJT0afoCvDStJXy683llncvzvLXyxlh2IivkRDSmxs229J/ZxnlczQEABE9u5Q6RVUdG2LxvxZ1rcigrYzzvEkF65CxAmKh+ZwLvQjZXuQ9SKM6n60kluuSmKP458xrcdeGF2Od2TYJb1wTqZ5sS8zO2y7dAXuWzzphcMbjQSRO6n9T2B8bR7odr41dAAQXkj4MlUdjH08w35mVm0LQov6ZGThtnCwBNStakcYtEGkohuwMWGlFbvVO2ObXADn2UUSCaCiiggMMK2eLguCM9cIe7UNq2TDygePNIBFTEh3jnNaB7TxdPCET//6QiH7eNSdx45lV3MgkvXcPjr5PVouZQbgbHQ9xkt5RiuNR4B5R/1zaHs2pXZavArNrLzM9LvYPjeW653/fslj0Te2+S0eOxvuiKCbVhiwnF97KxYhtPUxMvqMPEXAxztKjL+H4WiKMCPlgcXFfvDzc+dNnBbIkXEc5ov92pXBvMDYIL4T2/QXr4vBwrWMyRElklEk0Ts0y1i+xpi6yKXZL3ckaokEO7BRxuKBBNBRRQwMRg0OjkvwzkZ9HECBxqdz2j8UfXbz4JKLjwMQAvanHo4GwkB8+DY3ddLs2PExmuejLXxQm4A1LD88nuYGP9XOw3ltndmiZVRBjnczSNX3l9LLLGTMCtq/LAG3mOLOeOpP8NcZNycquy27jb+XPaERxy34eXZZRpw5cSnT9JQtbo5mMKNLFrOLji8IRMmg4Osm4ALMY1nqFyfgAAiTvG30YBBUwAROAAAC5DTJxDhsI7/4GBMSRbMCvPxoGD/DwzyzHWyifT9WQgDLMPf3JWQ9/w/klpp4BDi0KMpgIKKGCScailIMNm2LhQUgapgctB0q/qy0+kLwmZNJl5xVU5hGAFcVyygRcR0uL/VBSeD2668MPgnuTpqmmN7ONn09FxtJsL9O/7+IxcMgdjN3Zl+/uQq/JzfXbicTo0C/HOWpTuzxwgO6+mx43crfeyx/mYiHVUnk1l+15sYrflhvzHxo8ejTjrh79xM4CBcfZrA0IKm/gCsoLlk6jZ9ewHRzR9iEAPZUbZSYAzPiTujDMqcDIsvqYkEPZ9qfN8/sHKc0KOt15fjGr+NZazvhgrMYBq27Vd+vKbbCecoKOADwQFi6YCCijgg0XO2ZnyRTZ3n0MjANldTWr4LAAAO5xLFrLx3xNjqF9Gkml4SQAs7tqA4s41CPZty9gOwyWlBifwfKSq444PlSd8w3tzLjvR98HWUmqcrdlCvoe5hEDOlVTLaimX+Vs6ZFuJ/2/vzqMkOeo7gX+jqrr6nO45NRrNoRGXsQyYQw/jA+96MViwXsTugi2bZ3jPvGWxsQ3r9bNhtfbz+i27ZlkvNo/LeGEFsgABa0AcWoMQDyOQNBodI2k0t2ZG0zM9PTM909NndV2xf1RWdVVWHhGZkWd9P+/NdHdWZmRkZlRm5C8jIm2tJ91zIDo/64sv7jTS13qya7xSawtSN2o9P3WW7UolXJY801b9vC3KMiJ6fq8vvrhrzD3N45S1rjyUSiOL0yg02t2jWabcZKXrXDtY0nngpkRl2+LafjMtRkXfb12JWMeyWF3USMc+zfz+YAAq3RhoIqKQzAaKdC8aou/Jk38XLpVcOOrLmkde7fmyzdpYfgEWD/0lCpWIG5baWnjte84k7r9R4K7rW11QCrKOTdM/cn0F8MYz9wMAhipXPFezYfaASmasn3HcIAOjV59VWM6te5jaOtyZqPw4pRG8tUtfJdL1qa3b39bTzqQfUvd91f1a1Th/Bztk/3nHdOXVfsM1snTOWo/D9842b3V0aydXsesrXorlTWH3+W+NQiK8x6ABITM8GFTi1wxtqvtaNxAfFUPpu7zVttBcfyDSCbDKIetn5z+ftLt+DZzd7H4HBhUDTUSUqPA3dL2BlPYNnbQPOG373Om6PFSZBwCU1q56ryt25tZbLRXwkVuKmC+rnf7b41H4KdZWFOYyGWjSWJ3HtPW6u0OetHoomSwbEZUzv1cX+2yD574KyZ5iyfou9lINjNnnd/s7TsFvXKoTO1qfFOxB6fi3x/XIByoSJrrbuaehnjpvXig71N6gSyb4tc+M8poYmuj7RWEB+wPS1t9T5x7qfNaeo76ytzVntdiXkonHvZQPHKOJiNIl4A17/1L2Vhv+xi8dRLG6gJGFM4Hy4Jijvpt6/e3zf5AZZ5AjjnWprEOh8qLUSslU8MulRUyIAJTje1l8B//unu63rV6tubqWSmDM74mLT7p+Jh1+6yU8/3RO1GzLzP70zSUlpL1DbNdnvuMeWTtjdQPUxgZJLljXLLa6Fa9tuK7vM73vFwNJFJ3hpZmen5RdfV3SfceYMt3yKQnS4ffe7Rb1KoDxns+++tx/gZ8/ewhHhkcMrFcVg1ZZwxZNRGRYwECRbJ+ONJd37Trn3f3HiQAwunAmoktZmFRDVFJcAxNB0zSRF5sY6w55789fX34BAEDUHEcqbf2wD5yv2yIoxNsPVTmvQTMoqPW0OepyYSB9j9aYqusVtSGsnHkb5MFfCrS8P43M+QSJ2gGmRnlD8Oxo4U0M6Ru9egq7938MY/PPJJ2V1Mlsbz/R30rHe/4gM0S7c+bKYwCAZ4fH+j5bnX4rGit7XJd1HMTddr4+umkP3njLB7FQ1NxXJmRk7C9ioImIEiexdPQ/Y+non1p/hryA9N2Mud2cmbypSMONrOp6fcarcV3OWlp5M1zbmK0HKtwCAUrrMLU/vYInGq05XFvEmG4ZY7VCsefX+nvt/BuxdPyPUagIh26C7abvumU/HTfg8d60pKeb3YbzjwAARN+rtf2Pi9M+ayzdCNSDPoW2i3I/6Z9XR+ZPac1PZEKxrtJ1fJCl4xqiqjLVCsLUOuPjdRM9P3rp1Bn8Zwnj+OR2vOef/QG+vnV33wO2+uKLsXL6d1Gwt1wK8lbeQPnvf/ApZQG1hRcHSYxSjIEmIjJEdP2vQQKyMQE0R63lda9aPi2YAt9c66zb6aP4Wu8o3YAb6MJnSzDAMqa6qYWg8fphHcrlNuzrnn27eBUha5vbH7plwvrfu4VSf7ny65bl/bFxbi2yXBtcebdmVEkiKK23L9kDhO1x0uzB8yCJuU53ma9vtvgOsvOr0b2/v4XGmmNaG2b2o+zbtYlBKEqxbMVqAHh0u06BK8MTuDzcbi3p/N1vltwD8q3rp3f9MvjDETPnoqOb9ricR/3W675MY2jc9bN1bvvDfbuWDv83VM6+VSFtyhKO0UREIZmunOultz64t3PXOJOD17oGEyKvTJm/AVKve2g+4fJKV/b8wHolLU7O2+OcBxPdnVTGwQmQrMIB7JvD9bviFiR2u6mPOizjxvu77vZGtHSED/xyIV1+744vKexn+xvrxrehBECKoM8VvfPtW2ZCpK32LLTvpGLpzdmW099XWGOK74qJMkgrwB6zt978ZwCA6x/6q+CJ+FaPvLbf3L7xvDQEijP1L9RuUSuk2gtiiAC2aCKixEXTwqQ/QZ/uWmGSdiD61mu7IQrRwkFrBmm/2TbVbSpEiybfReOrnPYdp6Bcghymx4SSncCVSrDHpxbstulVtyeWqq1gkr650B+fLXrq6+4PEAZOCpWp61uL6I450llXiJWHpRINN5G/pIsrEcVOikKIALyLsC2WtcX3oFc0a/rJeT54NJT3xOsb5IUtmogoMd4D/qqKqcuS56o80jbS+MPEhbS14mZlBzB5EIVltwpWyHV5Lu7TzTEG7i3cej+RkP3N3rV2jS3AaFyQ4IkVdOz7LrRbNAmsnv0NlM4XAdzjsC77T+f0o6PXotBpK7tTEHEO+qTYg014zuyXmDvXcp9gJd3vW6/Udc7ImojSr3i1FSwuzicw+PKAEg2n4Er3+cTW+jRIMCaUYOe2XY98ArJgqwP2nG/76xfrl8v4Bzan7GKLJiIyI3BdPuQTael28+yXnIkLo3+zaP+xoSK+QNu70lz6JSyf/D2ULro8Z4izwULka1DYGM8bbcXogNM0K92Rq6f986AjRGCgv6LYH/irL/w0UJ2wfY6ev4O3jgvJpaKrvkfcWmAFy46eZCri6zdKptbvlo75sjB+6XArZc+bN/fgo1OOGHKirCqfKmPyG5MoP1NOOivKys1Wd6u1jH3zJmf2A9DvJpbUVjo+M/E45ZdqSxhaW3BdyN4Vr/cBiPqZVWV/NC6/UGGuAAlTKjDQREThKNy/DC+eVU7OVCMD2febiStTTN3dbESocX7s219As7IrRHrBCbfBm3Oh93gWZB3XPf5pbDv2Tdt8Ybe93f3QFmxReB2xfhDX+un1kh39VEPQ7WbrvK9MkRoj3fp3oexKK3AwsX+5qZmHW6n3vbHOd1HFvETX9XlobR4AUFqdd197wH1VdLzJIkovAYGhS0MQGbp+jlhB4krsXcr0uI5nqDLopKd46jymBl2319HWk+3aVoObsnjoA6id/JfmEqTUYdc5IorctQe/4Hqzp/1mK1fet8HrF2Lzt8XerZbCX5U7b50ywW0gZftsfjdwvgPJqAjROsf4cVQ5Tu6VRqcAablyOVSOnLNgH6NJv+tcuwysD6TvM799fDPFMqQm0Kj81g9py4G9xZVNX6MzEar73NLRP4MQTWzBx/1ndv0+aezLIHlthh+MvjLzJogrGwB82z0LcTfYCtGyb9vRr2PY9w10RBTWE1PX4ejGXfi7msA4qklnR4NugMj0YxjV9aqkL7zPl44feXWP6/9s1eqG98DW6wF5RS1PAICiy7p8sKdeZrBFExEZ4n6xEJAerXKcuxypr9W7QuDeiibqFk4puxJam9tuEh6Y2/FpH1/P3erSwiTA086og3vttZifN2S5sBZXe4Lp1trEreuc+bGB/Omn6d8dVW1d7SBT4NLSHIVsqLzqWZdbSzT3Lo/qKepvbW3+VZCr2zt/H960B7fvfaXvmozpynJfADxAwGn88lGUqovdiQTLFxF5Wi2V8Z5//l6cKg4nnRU9nSEZPGbRSzBMbmLkH5BymmOtWMKvveG/4PYb3K4LHukHae2W7gZy1IUtmogopPbVx9Rr3HW79/i1ymiLMLDkGfNwGztKamQpRCXFdiO26fT3sen09z0GxQ62TqXufZ2Kiq2FTKI8uldp5U915rDl0O2tcyrcyq934K8vsBN3zLazGs2ugKkoX222zKyOtn6ujAHQ68YlpNem2T7xPTbeO0n2fT8k/sM/+wOMX3wa2044jT9m8A5AKYikev4novil6iTsaz2E7xXM9+lLrs1EQnGcAL1bdC+WxzGu8Ra/YK3SRddv2SpbgypUiyYhxIeEEIeFEE8IIb4qhNjY9dn7hRDHhRBHhBC/0jX9FUKIJ63PPiJEq/YihBgWQtxlTX9ICLE3TN6IKB6Fxhqmzj6Aa5++K2AKpi4WbjfL9vRDXJC17uFsnXqWrgEAFGY3hF5v/+cqLavW+9wH2wN++1FnvILwvCoZlZk3ozb/ChQvKDxFbT+51HmTitPkuOo89jEUNNbb3mfS9t1wjy1638T3T454J/S1yHL52/W7n2DF1PYdFRd3YuXUv4c4c33frP77Ncbt6Hw/bHy7W8aNkSai9Mnq99Ij0BToAZTufjBxHo2plbXq8h67IEgpaZQn/GeixIXtOvddAC+SUr4EwFEA7wcAIcSNAG4F8FMAbgbwcSFE+32cnwDwTgDPt/7dbE1/B4ArUsrnAfgwgA+GzBsRxUAA2HTmfpRXLwVLwB680e4KYbuBc+uK19dixeQNkdeYPdZnS9uxePgvUJrZGMH6PQRdjeZyneOmtFzEA2PWNqMy8xYIhf5l5u6XIziejgEtt/LsUAZd01NrURZPLEGnLHiv2PcJZ4JxJqetbKzeYP6b0BeTUr3RUdw5IcZG0mcFQr1aS7rkJ3j3SiIaeK7XWd10wmfFzIpCZqR7cc99Imw/7b97J62XkWApULxCBZqklN+RsvPuxwcBtF9ldAuAL0op16SUJwEcB/BKIcQOAJNSygdk61UhnwPwpq5lPmv9/hUAr2m3diKiPAv5tN6zRUq3CPr3eGa1v8sJZNDXEidxIdU9Dv5d5zqBAJ9DEW+TaPV1Na8+BwBQmBuLKjO9HBtZhdk39qCDahCi3VXAtlysQYcw0lCVCL6vwnwfzJ0Vfb+0xg2tzmFq+sfYdvRrHnPZzv9hyiSrnESGZeMasf6AU6cFfLytuL255zNUDrqaTOtch2RtU9fKs1EGKBomBwP/bQD3WL/vBHCm67Npa9pO63f79J5lrODVVQBbDOaPiDIhqotSdi924W5/7N2lolqbS0syz2kpOiYK+6e5sBeLhz6A4pX+QJNqJUz9OPikE+Stc25Pad26mtn+HJlvjclTar8a3u1NklrH1a2C78F13LP1HJCd3/71GEtOZ36bzSfvxfilQ0rzOq1h0/SPMLTWPYaVTrCfiEifaDas35xOMIbGmDTwoCbES1OdUuv6NWiXwdZMq2fejtXpt6Kw5hJm6Elf53rtMWYWpZLvYOBCiHsBXOvw0W1Syq9b89wGoA7gzvZiDvNLj+leyzjl6Z1odb/Dnj17XPNORBkU9K1zvjfwTk16w/LKa7gbNPX1RLWs26nZecwW0feLlxRVDDzKm/Pg00WnWWNr3SNdj4vKPu09drotZUq1FVQBCFkH0DX+VWLdAzIUZYit9Ve4rsfun/cGI/uWcummOjn7GDD7mGaeFLJjn+Bw/s9Q6SDKtax1UJk8vx+NoVHvt/Rma5PUeF5iVc6orZ0iGxOoL74YwP9Dz47qvg5mpkU0heEbaJJS/rLX50KItwP4VQCvsbrDAa2WSru7ZtsF4Jw1fZfD9O5lpoUQJQBTAC675OlTAD4FADfddBNLKtFAU7tR6p9L/9ThfmOu8fSn88ISGepGaGL2AEpV/7dVrV/L9WpFqnNvPfYNLG95oWIKivsv1rN67420aNQgi0OR5aNQX1Of2atVWIA4U9+bdDp/ui3cO39xrhVkG7o41Pt5qAq3xsK2suz99rWuBbJ+Q2C0Qm4oLXsruM6j9Yi/vPaR6zWeshMRqSg069hy+vvhE0pLMKWdjbrm8A2O1069C2p98UaUNz8AUY3oQpyxIOagCfvWuZsB/AmAN0opV7o+uhvArdab5G5Aa9DvfVLKGQCLQohXWeMvvQ3A17uWebv1+5sB3NcVuCKinLIHb+x/+3a7SG13GbeuZGaaXW89+R1sPPug87q88qHK7/RrXdwn5g5ju+cYKg5pur4VTF11fDsAoDk0GjiNbs5d2oJ0AXM2dfYBAEChUdFI02k17bGwAoxJ09n/7Uu/3v4vnytj0xc3ofxs2XFx/YH8dfm1XlRdfxJVi3haOAYe/l51mC6f7Uj6LJz0+oloMAhIjVbByZ6ZRG0IlfNvRPOpm/1ndk4BQR/crM3+KpaO3oZCzeRoPev44od0823R5OOjaLWf/67VLPJBKeW7pJQHhRBfAvA0Wl3q3i2lbHd4/R0AtwMYRWtMp/a4Tp8GcIcQ4jhaLZluDZk3IsqCvqa6vRO2Hf8m5l6tkIDreDFw/lzhjWT+YrphDXMDb/jm38RA3X0VgxCHoj48FS4zpo6h3342HYQJ8BRPWJdhWejt/ifXll2W6P9uFVajqSwGo7ZPk6qIbvzyRo/HeSbKg8J2+Q3S79sg1C1AnmQrRHTly2v0BV28YSHKq9Xp30RxdBrAwRjXmp72ErUrP4ehtaMOn0iX351nc76eem1nEbKxweNznnfzLFSgSUr5PI/PPgDgAw7T9wN4kcP0CoC3hMkPEWVRxBdikzf4bmkFarobdwUk7ou5DLHO+PaNcLtxdsiDiSCb1uCdTvNagQP/ZBzy32y9JFaKVqCpUF/F1PSPcOYHXwaeu7N7Tvf1m6axDucxs1qf2OdsfayYeETbWVxaD+iZGNDf/W+Vz4K+f05n3Ukw2VKMiPKqvvgS1BdfAqdAU6XUaqXbUDpveM9TW3gJ0BwCcKprTofzlNmRvCPRWN6JwugsRLUA8+d+0924KY3S9GiSiAaQFLbTkHZgyG0MJrsILkZaWQ1zUTVwQVbc/GZx2H8mLx5Z7RsjqLNIiGMTaXct9bRNBKGUhOgq2n6TjiyUOilsmv4xmosX7Ym7/B2BMF/LvuJk74abQQq73Ox2+bXEM7oyYzrfN99xxnSkdGOJKFJ//bJfwx0vfB2OlzXGMHKpe1TO/iYqMyrtJuK6QgVZT2uZ2tnXYfmZ96CwXOr7LP489SovnTeQD4pa2K5zREShFGu93XZ0b9iFvWuH7wLtwUbU1rPt6N0oKgy63bcal+3onxrTuDaq7OP3uDb0CTDWi30gaxNBotA3mB7lR6v1kWI3rtD1K6srVN92C4+/Omt3WdY2V8K9olwN+LCNsu+XQEun2vanvwRZ8HoG6t/ysH+66pcuk6FJotSKftw+M64OT+DzL3wd9ux72rdq2KhcBwAoXO3qgq74Qg3zdMfrc5/fuW5SRHNth5VeOh7q9S/D83aaMdBERIky9lLq9oW+b3GX9BRXM375SLD8xFi/mjq3D5WNN6BYXfKYS/Vi3Mp4oaHxdjTt9UVcMZBSPQDV9xY3rzEKvA/q6rm3QNY3YEvnHRc9C6vlp5vnUAghBlTXXiRjFbm+7GYs/3a6wXSzK2/97xPYNGV04bTinO0WTe0Wrf35y/hRJ6IUqi+8FMuV67B5+u/RLPrM7HmtjWmcvlDzW3ri9uHPrD1bHjg5tQdnlCwGmogo49L6xMzceE5+S7RbhRXqDm80CxpUsI+1Gz7hvmWiqx4EGR8q/Fvn6ldfobnOIFRbyoXn+wTTxBgToZKQLn+Gf6thYhyKYf84H8G3qx0wEn6DhPet0zVTvXPFtcuNnpdaeL9CRP4EmtXtirNm8BoEh7OogGKLYr+TaFcaYVu7dXpP88SdZgw0EVG66F58VJ/2991FhA+UhBrDx7ad1z1xu3OgKDTVMaysuYXtJj2Wa7iJYxOUoXXFdoNtIIDpO6vmQe9rcx/VzvBLV6+sx61RuRbN6hYAEY4t4Xr+FLaf9vlcjnnfKc/vfBvv3pepPdpElGshAyVmqlYRn/8ck483sLN28TUY3vY9h1zw3J8FDDQRUeK2HbsbQytzAExePFzePNUYan1aL6Iz3k1ISm1MfMY2Kq9cdJweWqdrmN44Jf0tHgwEntrdXOzBrBRwyon7W85UU3BiZkypTssUnX3oUzF2HU8j6009UpL/lZPvtX770PpErRhdkO+Nbd72GGxBb5KsfVkvTzjmLHba5zf/pIjItHScg2MX9DSbcN3I82h5nWsDHWbvhaqXXovqpddiS/d1UzU/lDgGmogoceNzAcdB6qYyxg6A5oUbURufwOiRswCm9dbhmrTKQNLBW0PFy1QQqL/7mttb54yTMkDPuXB5iqRi6JBklDfDfePwBHwDZGRcv1NqrXGSrrz30IoVmcv3+KWnsTq5G5um71ddufWztY8rk3sAANWJHc6zx13nN7hveL9CRHpi6F4emMO6vSNIXku6Tm19pLmdJk62fDKQCV6v9iAiSoWxh8cw++FZx8+E62CwbgqoXf5FiGb4C533jWtaLoKawZ3OoOox5r8vwKGxbNjm627LR7b9htP1aSkXi9hWHVersZTTKEL2+5xCs4Zrjn8TxdpKb2KuPeHs0TqffdsZRiquY2AieJ2WczURZYfCecPz+mz+vDMxewCl1TmHVbmvq7cO5BVMCp6vzrrCJ9EiJcyc+ylqbNFERKk3+tQoqqeqwEudPlUbM6QdiAp3/2OiYmBr5RN1I5DVhdZ6mnW1+SO9aMdVMejdqbJZhCg0fOZ1apVmrlvaOlPbHiYd52XdX0WtOn5P4Awp6Xuv2Opo65ermwHMAqL1CiAh3Y61W7qtlK97/NMG3rYYg2q59XN+GwD7TYXuGHch89JXNNqBq5iDN2yORJReA/r1jHuzt578TkQpC5i+wIdvYWwfioHSiIEmIso1k5cgnbRcx/cR6z/iuBVbuf9zuLZcxcjCs2oLtPMXRYuehB5ALR27DaJQxxb8rcdcKpkK3oLN3H33ILe+6N12sTSB5RN/iPHTpwHMQhZagaZCo2qbX63AlSuXzWRThdZ3wLbdq6NYOvEfMfHsSQA/6Jt76ehtkLKILfhYqCz2Eg45cZ8vNp0WrfZs8OaDKAmLR/8UAk1swSeTzkpmRffAT7Vl+3pONJeMzXqdKm05o27sOkdE2RZwsGujVNbtO2ZTRPmorWJy9jHlre50qbEGAzebS9uNv2viIbvD2ZdvjkHWJ/0Wyog439AXdKBlvZBsYFKiWb2m0yJpyzP/iA3nH8Pw4tngaXbZfPJeXHP4H4yk5arzldA7jrK6DZDOVTjZ2AA0x2wrCEr1JQDtp8ve1crG6s6Q+enVvNp6g59oNrqzQURJaYy3zkHdBvZ7qdIVLeLKh8b123tO520x+UISPQNbqDKFLZqIKONUWy3Yu6wFuUjZlvFKI84xjgyua/uhr2B14/UoNiq2dbR/ZCAiE2iMpyDBwnUZ2Csd0ZXMqPuBug1R2tr7Q2tXseXUvcrJNVZ3ozh6xvXzydnH9PIXmejPJW7fa+l7M2QLGls3NY2hCQD9LcRWTr8LEDVjra1WH/wCtg83UF650JdN/b2WpW8xUYbk8KuV+k1KMA6z8/H/jerYNqV5A3V9616ErVdTjS2aiGgwpOZa5NeaJzgTSZZqS9hw8WD/B30NaYIH6tZvatPzRErlLSsqRahYXQqdl9Xp30Jt/mUQK06XaO9xlMYvPe2RcrBxpKI9SsFT93+S2m5l4/zpyrO/jeWT7w68/ri4N3aK46TmvY/dPnUdC08OdbW2Ck+uzGNqZl+gJ/Ftib4YiijX+OVKjOnLg0adb6hyBeOXj/olGCAT3U8SOBh4FrBFExFlWudm0+WpRjuoIayuYMldlOxjiRisgFnbnobLbWNlDwqTBwGH8Zi1B5sOSaVJt+c8modo25GvYXj5fOjEmpVdqMz8OgT+Wi8DALYe/xa2nrhHe7kWtxZ73gPtx8clf0GfaDZH0azsDpclUzTeUuBVZvvH5vYJsmgfQ7V8Rn8ucsl3gCIZ2wvyiAZW3r9kZq+R4QfKdqaeatDjFfUQFopvyKPUYIsmIso2v6cs7ZvQvvmC3JHoBErCXQSbtSk06xN6C0XZXU8x6cq5X8fyyd9HoepxeYm8qbNW37nWD8c86ZWZ8SvHUKouaqzboK5B5teDqi4zKRpemgEAjM6fDJ4vX0ncgKTopifhrExcfApA9wDqNp3BtjUzyugNEeVeiDpXvfXyCnn+RkN5sZGuf7gQPnXI4C9DMbVIt3Ywjm+dSze2aCKi1Fk5/e/QrG3EFnwmdFpDK5daPytXQqelRbm7i/OMy8ffBwDYhv9pLEsmuV7aZRnNituAvz41Cynj72/vMQaN2ZxEtF0RNh8fXrmAPQ99GAVZN552EO5bqDo+W4qegNqz4tl/y+UzA9+VjWd+iKnpH6MgHZog6uQl8HzJY9c5IoqGx1WrKbB46L9j9MoJAKfjy1IfEyfAuOptXetpuj1QozRhoImIUqex8lyNub0vkhsuHMDw0gyGO4PFxnVB1B/fp1dan9KEeUuY8wR744eomo07y9hdZqhWa/rLBgkyxTdgvHfXuWAD/meAwe1qtYDzCjJlbR9mLb9ElFu+52oBpzqV/jXUfKt6lTXoM5enLSe/g6trVzE6f8pYmmQeu84RUbb59ZwD1oNMAIYXpgEAxeqygZW53+g6VR165+UNUT9D+yTQjbg92iXg3pIkQPKpHVLbpJhz0enWlXMxBxcdaZZ50UxHK7iWtAbtiYj6qT9s85ovzvNe/FfhUm0ZW07fF/ODSdLFQBMRZZzeRWbTmR9i4Qt/hKG1+Wiy02EfuNj743CirFCYTDvewcDVtAOAKrOmqUITYcAyI2MeCIff/OZUmj0zYtgQ5a6ZrfnWx3pK+rsi8nOYibIuI9eU7MnDflXfhvrcTwMAihdGo8oMGcZAExFlmu7TDAGJ5tUZo3lI+pZqXXQ56Xu7X6igS/tNgG5vjgq7HRrLew12nKrAUgxCb268ld5S5TIAoLw86zKHT7A3I0rVBQB+Xdzi4T7QPBHRgDFyTYnquhm+jhbtGvXz11zeg8VDf4nC8pD2spQMBpqIKKfMX7x1Xx3erVkstxYReTnt+r02Pbq0/eisW71VTNA1REV/H8U3XGc8EZ3RhTO47sBnsOHCgVjWl5TyykUAQKG+pr1s6GNhO8eNXzoMACi1X67gWqjS8B1RldEIJFHaDdrDGjvP06CJfWN6/w748SLj8nLHQ0SDKhUVGf/WMJWNewEAK1teaC2Rhnz7E02rFUWnNYWBG0i/m9M4WzRlXLBAWbTiGwwcKK/OpWjLg1ud/i1UL/8sivNFhbm9B0GPRrsVYus8UKhXXObL8HeP3XuIKIdiO7OpnkPt82nW+bJSfyYGmogo85K74AS5eGetRdPk7GOYOvsAps7tA9D9hjiT+13Y/jI1KHjiCUQozBhNbiU36PZGdPxMyUAAQVa3YW32FvMjC62W0Vi7Bs0TPxcwAbcxzARcPiAiGixu1xiVS+EgnUIdA0qDtAMGT7bueIiIqF+EN9JCNrDpzP0odN4i5TO+klqqLpMNtWjSWlyndUjKAijdlIqAav4Vy5PLcYqzRZMn13KUkvwBCJMXpXsYKbDyzB8CV3aFXIlPYDPFXw1lqWgdS0TZofDa4ziYWI9nHaj12cjV09h67Bvus2mdQ3m+HQQMNBFRpmVviJAIMxbDdXvIGpul0IhyvJgYKyAeq0ptEeqWqrqafmZkYyTwsvrStLNC5CXWgEia9hkwObMfAFCsrZhPPAMt34iIgtN96UnvZ9ce+hIm5g57JB/gHMrTbq6Vks4AEZFJstB6G0WzOOIzZ+Q5cZ4cSdez+Gw+9T2MXT7aGaDYpPFLh7C89ScNpBTtUzUZ2w2pThTMOU+LR/4cgMQWfNRQnsxaOfUOlCaPQtT2AYjqrWrs5hX18Pxx7dnJ2ccwOftYTGsjIgohrmqe53qEfkY06zhBusuLbFaBSRMDTUSUK+0xhIrVxTjWFmCJdvcww1mJSaFZx9j8yWAL+2zz1hPfxuZT98UcDmhlqj945JSLNBw0ex588tTUD7iaGalJcV21zahd+QUA+wKu1UOnK2bTvlbz64qQWGttx/DJYfsnMay9va+yEqTL1rElGgxZOX8YlqWuc0QRYNc5IsqZMIMkh9NY26Yxt7n8yQS3OQjp8BsACNlEsW6iS4zbAMZeslIjjDKfhspPSrogrQ+8bw80pSN/qgrVAjbfsRmjj44CcM+951YF3WSfIlG+3OpCW7oSMP0UmLjwBMpL57F28N6ks0JEeSTdT8ChxjRUumQHuK5H3D1bBmllRZnEFk1ElEvhBqvWXVmrorBy6ndRKC1iCz7rNXM8eUqlKI6JQ5qy/cN/X7uXk/7p4Zp6D0ClKooB4kMYqswDAErVJWtKmo6BZteEevf8GtsR+jxoX7433xsfO4m189sxPFPr+yxJUqjvpVJtGdc9dQcurMxHmSWiAZamc2+cPLY7tjqqcPzVexGNc7mUGod3UMvB4GKgiYgoINFsjSkjRbE1oTmKZnU0gYy03wRnb7mRVuFvSAu1VTSHRlGsrRrID6CXpyRvqGN4k1qMmycgW1uk+IBzYvZxjF05oZT21NkHMbw0g9Grp8NkMWIZqXj33Xi08i0AjMzUkaYgU7+M7GOi3Erz+SFC7ZZMzWISK3f81XsRlzfJRnr4BrRsDAh2nSOiVBLNWtJZ8CVkHQAgC2EqEeEvsqLR2lfllQuh08qK4aVz1m8OLY+MDAae1jGa7ALkyaXIJVLd08z+1pPfxdj8M0rzCkiMXj2ln6cELH7lNmw78rUI1xDw6Lo+defNARGRF7E8jLWLr0X96OsTzITfx+1xKqMPCfCqMXjYoomIUmfHE59FsbYccOkYL2U6LwXrfNA7npKJJ0XlymVsf/pLGF6cDp/YoOmMddweODriYFIaG0MFNbcdjdWdKB9/DoCnsfX4t/Dshp/CUCXIgD2DXQVtXDqF8Ssb9RbS6N5gckS4bBjs8kRE0fN7qCUgUL30Goys9beqTeYM1b/WkflTWNz+0kjeJGyXlasHmcMWTUSUOsMrF1DSDDSVVi/bpgzWJW104TQKMqrXw5sS9zFRqcoFeatWGm5io8yDWtqiMYSVU78PsTwBoPW9Xf7HD2t24Rys72m/AMdRKxgabv/KjB2fWMfmIyI1KXlBhL8kzh8R7xvZ+c/R+JVj2L3/oxhZPGtgZTrb4p6nxupu1FeuD58dShxbNBFR5l33xO0ori0mnQ0l/V0Cs1IBU3PtU3diectPJJwL/bfOxVa9DD02M2+k88H/OErJZ4Fm6J9jy8uzEeSDiDJPNoEYupnp8TjHKZz+inXvsS79X4SiVi9xHpCgf+rKqXdbv31IIQVKMwaaiCjz4mjyG9bwwjTWJndhw+zjAPL75H1k6RxGOuMn2cS1yZ31aLRoss/qtWiYuk4q6kl+mdA8UKnYpnxZPPwXAICt+BuPuRTeqqgxryOt75LzmpMSNAd7HvowBLLyYgUiUjF6+RiapfAva9l54DNYm7hOce6oKz3RpG8P/pgfDFzjtaCO8ll/ziMGmoiIYrD1xD2Y3/3zDm/ASv6GLHaRN6NXr4Ro3YiHCQ4GWVRnGaVdqpqg6vExWNnLTNeKmMiy14c6CbV+BN69YZfPnoL1kgciCis9AYHtR79mJJ2hypWA4xBqUK5r+J+YJUSOTt/pKU+kJm1t/4iIwknkhtV/nUNr89h2/Fua49fkTRSVhJDH261C55nV/FTbegQMpIV7MJnDMTG8NGMOYsTcVVMuXQsAEBU+xyQiIgcCYNBoMLAmQET5FMsNLC+U6RbVYOCUTcl+X3c+9ncoNNaMpSe1gurxlO/G9KtQqbwa44v3xbI+IqLcUT63+1/T/N6MlzhWvXKNgSYiIsowp0pUa1qocQUclzVRYYuoVpVgXTLMFo0sTmN103NbA6zm3NDavPK82w99CYW6W1AqvrfO6SuguXZtzOskojRiDCF5Ol3nEglKpTwORuEw0ERElIjBG/sk1TRahyTQwSzVwmzZtmPfQH1kIwpxdylLuf6x3DKG5zWigZffq54JEe8do636DZ3QnfLEa0WucYwmIqIkDGANLP6nZREP8K0lS7Upxbwa2HWFZi0Tb43MvpjKX07fpklE1MfE+S6VL8Jo5Un/7cjqbaf0l9Gdl9KALZqIKGeydiFSy+/mk/diaHUu4rzkg37lqP91vt4rCFPGIroRN1Lsg+Ytuu/c7v0fgxRmn4mZf1VzyzWH/wGNoZFoEu8iOocpvnOd1vcjFbKWX6L8GZhvYbOhv8yAx+RDPXgc8H2XJQw0ERElQu9KOTn7WET5yDqvqqx/Ndde2RlZeBarm5/vWHE00yLLTNVb68Y/SCVYQRwt1Ir1lcjXYcrY/Imks9CvExTN/xhYLbwDIUqdVLbaCa/9EER0df1euvu/4vl7dyeVpeyQrn9QjjDQRESUoOy1FDDBxDYbrphYFeFtx76J+vAk5utVw6s0lF/N1lq7Hv1kTyWYBotsj5DALm1EFLcBOe8MVdZbm9fPHcLwNcPhEoxsv0Vc3/RN3mm7dPI0GOUpTxhoIqJcyc5lKDs5Ncat8mT8aafGQOu2PBVkHeXKZZ+FEgwOau6rUnUxooxQctplVqmAAwAHWyciMqzYWMO2I1/DyOJ0gKWjrQP2Xh001xVRFae8fAEArGEg+EKcQcBAExHl1AAGcrIi6opFoDFs1G/a45PRp5qpNzjbP3blBCbP7cPUuYeSzkrseAUgIi0BThrjV46Zz0dSwrak8ll8fO4QysuzKFcuo1EataYOzvV4EPGtc0SULzkdC4B06FSWMnY7GqoiqLis9ncoG9+5IIPEZ52AxOZnf4BivWI44Wwc85bBO+5Eg25oOf9vM5249DQKtWVMXHhSbYGoT9s+6Qugq8V4kPNylq47BLBFExHlVNovR538ZeqGLcd4HAIEsbJ9A7/jyTuwtuG6pLMRXBJFdgCDdUSUPTsO3olmKfq3gCapVF3Enkc+7jNXDBeKENcFnSUbwxsAAM1iOfD6KF5s0UREuTI+dwSl1TlMzjycdFa88YYtMltOfgflpfMorV31n9k6DnpHI+NBqYxn35Th5fOYPP9o0tnQF+u5Iw/nKRZ4omTF/x0sNGscozAk2XkAp3od0DjOgYY4aGkOjWkvQ8lgiyYiypVifRW7Dnwm6WyQh6jftDe6cAajT92hNK8IMLAy71uJ0o9fUyKiMFpnURHJ84YQicqGuWxQpNiiiYgoUYN0O5Tm1hEKxyEV2U9FJoiIiChDgj/kS1e9YxDHW8wqBpqIiBLBC2U6ug+ylZKddmWU+47ShmOuEVEmRHWu6qpfBa1rtc+jissHW4v6UuXFc60leH7PDAaaiIgoHmmIK9mFGCcgCbK2BgAYC/BK5aHKPABg5OppQ5kxkwyRMakIXhMR+YnxXBU4MKM7RpN+2kIj7UKz1rMspR/HaCIiopi1Kgml6gIAoFhbCZtUzIKvNPT4VPU17H7kYyjUVrXTHapcwa5HPo5ibTlcHvqw0peMOPe77rpYJoiI0kQ9qNM6f0vNYJDWWV+ztRQAlCpXgKnrUWis6ayJEsRAExFRkgaqCXBvhWLq7EMor1zC6JXjYZMKTFiDSqpVXMKsVH9Zt5LRE5jTbMFRMhpkYuuR3NNuIcQyQURtPB/4kTFWAf0fSPUer0JjDU2MR9N1LkCgafOp+zB25QSGl2d11kQJYqCJiCi0ADUF1r8gIDEWJMhkUKm6iE2n7sP45aPKy+T10Ok0Yac04PEiIiIfAbsUX3voy1jZ/DwU66s+c+qnX6i3Hu5tPPtj9WVkA2Pzz2ivi5LDQBMRUSKk9f/gtGgyuqUGE5s6/0hyKycKjOWQiDJgoFpuO/Ha/vTum1J1AZPnH40kbSEb2PvghwDwkUmeMdBERETZFaCGMrx4DiVrYGwdqawORlKBT+WWUger5f5YholIl+lzq3t6Iq4XF0Qe5Bukx6Wki4EmIqIEDGY3pXRs846DdyadBSJLmIHl45SO7y4RUf4ZON92JcFAECWlkHQGiIgG0dTZBwEAQ2vzyWYkCSafsMVag0rhzTZf5x4Aq936srbP+L0goqwycL4Nk4RmHa39Jtvy6lyIlVIesUUTEVECxi8fxbjVP50CSPI+cuDHmyAiIiJvGasr6Dy4krJTFxpemsG1Bz+P4cVzEWWMsoqBJiKisDJWl6CATAS3cllWcrlR6ceAp4fB7JxMlE6Dfq5K4GzkuMrojsPI4tnI0qbsYtc5IqKgFJ7+jM0djiEjGZH5bl7B85+panamMjt4shBCkakKgqV/fxHlUuav+aak6XzoL+6jlq29QzrYoomIKCJ79v01RLOedDaI1AS8KZCsJVIHbyyJyJKqgHMSeD6kwcZAExFRRArNWtJZGABZq8jq5DftldS0588ua/mloHqPdNbOEUQ0OLJ6fpLIbt4pLgw0ERFRzExUTuIJGmx/+ksoVhdtU9NTuUpPTig2jJd5WN85/G4QJYxd5zyked+onD15hiV/HKOJiIhiYrJiFU8lZ3ThNMqVy9ZfJvJvtnKZjqoqK5zBpeMIuuKNIhGFxUuEltLaPACgcXU2RCrS5Xd3gcb/4yWCPDDQREQU1MCPPxCQ0f2WQC2Hhx0AILJawczN8cvNhpjHczMRZVSpugQAqB78roHUeC6k5DDQREREpEGGqrhlodKXhTwGkNXAWIYkHXzMacklogxLzQszsv6QjzLHSKBJCPFHQggphNjaNe39QojjQogjQohf6Zr+CiHEk9ZnHxGiVeqFEMNCiLus6Q8JIfaayBsRUWTYrURbeek8th27O+lsBBPmeGeiqGQik0RElCGCIWB3kbe+5HWdkhN6MHAhxG4ArwXwbNe0GwHcCuCnAFwH4F4hxAuklA0AnwDwTgAPAvg2gJsB3APgHQCuSCmfJ4S4FcAHAfx62PwREVE6CADXPXVHBKkSDZIkuotm43tWrC4DAAr1SsI5IaKsifUsF9tpvGurEng4es2Rr6JZHI59vZQOJlo0fRjAH6P3K3MLgC9KKdeklCcBHAfwSiHEDgCTUsoHpJQSwOcAvKlrmc9av38FwGvarZ2IiIh6xV9hSuMFKdk88UlpcGksTU6cj7Fr91HZAACIhFp7Tp17EFuPfxvjc4cTWT8R9ZO8VvTLyiUgpLErxzFx6WDS2aCEhAo0CSHeCOCslPKA7aOdAM50/T1tTdtp/W6f3rOMlLIO4CqALWHyR0REZF7ENUR2yYxYmmr4+TrWW0/cg6mzD2J4cdp/5ggI2cTEpYOpOsJEg27Qu84FH7vO8PWB7TcoZr5d54QQ9wK41uGj2wD8JwCvc1rMYZr0mO61jFOe3olW9zvs2bPHaRYiIiLDwlT6NJZNujKY9PojY7bSvnbwu9i6JeDzsJzu4lJtGZvO/DDpbBBRKuQrkB6U42DgQXdNsw4UNEe+4WGghPiWVCnlLztNF0K8GMANAA5YPdx2AXhUCPFKtFoq7e6afReAc9b0XQ7T0bXMtBCiBGAKwGWXPH0KwKcA4KabbuLXh4gSltO7RjIoA2WELam0rP7wdmx56UvDJcJdTkR5l4HLX1bsfuTjkLqBJqKEBO46J6V8Ukp5jZRyr5RyL1qBopdLKc8DuBvArdab5G4A8HwA+6SUMwAWhRCvssZfehuAr1tJ3g3g7dbvbwZwnzWOExERUY9Cs976RTbjX3luW/wExf2RDO53IkovnqHMKzbWUKotR7wWnSPHW3VyF0lIVEp5UAjxJQBPA6gDeLf1xjkA+B0AtwMYRettc/dY0z8N4A4hxHG0WjLdGkXeiIgo+zafvBelyhWMzj8T30pt9anJsw9hYefPxLf+mKhXG1nBJCIiZ7xCeIl67wRJn0eMzDIWaLJaNXX//QEAH3CYbz+AFzlMrwB4i6n8EBFRfhUbFWya/lGiedh85p+w+cw/Kc7NChzRzsf+FoePnwZ2b0o6K0REKRBNuy/R0+qb9Q9KRqi3zhEREQ2OmCprWes1zv4RARjYaRnc70NrC2jMnY5lXRn7FhHlTgZPUbmx+dkfYOrsAxifO6y/MA8cGcLRxIiIiLSEqYVloQanmEfeyRsQYCdmLRCZOO4vIkpS0Ot+8PpCoVHFpjP36y3EUyUZxhZNREQh8dpMeSBYkslHNstIFoK7RHk2mN9BofBQIItnVCJVDDQREQXGKgJR1sjc3PTkZTuiwb1DREmSnm+oTXP9UeHsyZa1pICBJiIiojTxrJy6LmQ8G/mTnopx9oJdWcsvEVFaBL32pOeaRRQEA01ERERKYq70ZeaJYVaCEGnMp/4xFs0aAKBQr5jOTL/MlEEiopBkM+kcRCyN10DKMw4GTkREpEHmvq6muoHZDEJkc5yhdaNXT2HTqe9jw8UnYl939lpiERGpyva1wV1et4vSjoEmIiKiNJEyYPc5UpH1YIkAMHV+f8xr5Y0KEQWVjXNudLn0SDlVuyZAC9sIckH5wa5zREREWvJetUouqDA1/SMUaqsRpc5gCRFRbDLS9XbT6R9ANKrG0/V661y8tYi811kordiiiYgoIF66B4vKq4rzIbmSvWn6x9g0/ePE1h8bnjyIiFJhamYfpmb2JZ2NGAxKHYbSgi2aiIiIIpeFCl6wPGa9K1qSsrPnhPV/FsoxEaXBxMWnAAC1kw8nnJM0y85VgEgXA01ERAHxlmtQsWLYwm9AcFkpQzzGRBRMefUS9j74ITQXLyadlfSJtYV0Vq43lDcMNBERESkJUTHM0v26bp2UddjgslQuiIgoe3iNpoRwjCYiIqKIbbhwALWxLZg6+0DSWXGX86AH69pERBSXUmUeADA+dzjZjLQNzDiTlBZs0URERBSxQrOGrc/8I4qNtaSzQqkSTcVf1CuRpEtERGpKtSVc/9BfYcPs41rLTcwegGhUUX2GY1tRtrFFExERkQ6R97YxatuX970QpUJ9tfUzgsDj1b//A7zoJ55nPF0iItIjZNNvjr4p5cplXP/w3+DK0hyA3SZyoTW3zH0dh+LCQBMRxW5o+SJq49uSzkZovBQPmFQ2O09jnlIuBZXoqZn9KNYrmLjwpPG05dIcig0TNyddUrDPiIjyI43X7iB5SuN2UFow0EREsdvx9OfRKI0mnQ2iHEhDACANeVCQokChkE1suPBE0tnwl55dpi3DWSei3MvIdZMoBAaaiCh2hUYVhUY16WwQBZTXCqLmrTnv5AdYXr8DRET5Ih1+M4fXAnLHwcCJiIiIKFd4+0NE6RXjkxrFrs9CK0t80kT+GGgiIiLSwOpVG/cEERFRFghesylmDDQREYXGZ+eDwaqkRT4wMiuDRERERJRdHKOJiCgwBgQGSTrDiWkog+ncM0RENBhEswZZGEo6GyllXaOVqwvq13SpNbe+zae+h+rYNRGugaLEQBMREUVm98MfSToLpEnyVfbUkYZAJhGRt92PfBwyix11Yr3e+p3P03e+nzz/aNJZoBAYaCIiosgUG2tJZ8EcGVclLKlAT/oqmURERH4y9ybj2OoTwMjCGTTmnsXGs/fHtk4igGM0ERERaYorEMTATzTYYouIiAZDoVnD4pffj+GVC0lnhQYMA01EREQ0ABi4M4axOiIiIvLAQBMRUVi86aJI+BesoZVLAIANFw6EXxvjMOQnxu4eRERElF0co4mIKCjecw2YuA64+ntcSrVl7H3wQ4bXrxo55ReAiIjyrbh2FY3hKaNpCtls/UzlOJZ8ekpmMNBERERECBw44lvqiIgop3Ye+AykKBpNc2j1Ejad+j7G5w75zluoraA5NGZ0/c748IjMYqCJiIgyb+XUuyCbZWzBndGvjIGVFtZJiYgo5wrNOoB66HQ2n7wXUrRGrREAps7vV1pu54H/g3p5IvT6o8H6ELljoImIiDKvsbrX9bPtT98FWTDwNJLj0xAREVEAk7OPBVquWF9Bsb5iODceFGJHApLPmsgXA01ERJRrowvPJp0FIiIiovRi5IgM41vniIiIKADWStOgVJlPOgtEREREPdiiiYiM23LiHgDAfLLZiMzw4jkAwOjVkxi/+BQ2nbkfMwDKS+dQnbjO2HoK9VVjaQ2CxuWzkaa/8eyP0SyNYuLCk5GuZ8dTd2J5608i7kBOwXr7zfjlo0rzDy+fb/1cmFZex/DCNNYmd+lnzoDO98l6209e7HjqDjSGohu/Y/Lcwxi7fAQAUKy1um+MXT5mLP1CLZrz3Nbj38LVnT+D4aWZSNInIhfNRtI5UNK4Em2dQcfolRNY3fTcRPPQqQPMHfGdd/zi01ja/tMQMHc9HZs73Pl9aOWisXQpOUJmfMyJm266Se7frzaYGhFRWIuVGsqlAoZL/WP+rNUbqNab2DAyFHo9K9U6CkJgZMjsm07yammtjqGicDwuWffgM3NoSomfe+7WyNd1daWGiZESigW1AT4vL1exebysnH613sRavWHkO6Lr0tIa7nlyBr/1s3tjX3eezK9UMTkyhIJiGfHC8xxRvqxWW0Gm0XK6v9NLa3WUCuk599QaTazWGphM4NrYTbUOUG80sbzWwNSYmfxeXa1hrFzEULGQ6/pcVgkhHpFS3qS9HANNRERERERERETULWigiWM0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREQw0ERERERERERGREUJKmXQeQhFCXARwOul8GLIVwKWkM0GpwfJA3VgeqBvLA3VjeaBuLA/UjeWB7FgmqJtfebheSrlNN9HMB5ryRAixX0p5U9L5oHRgeaBuLA/UjeWBurE8UDeWB+rG8kB2LBPULarywK5zRERERERERERkBANNRERERERERERkBANN6fKppDNAqcLyQN1YHqgbywN1Y3mgbiwP1I3lgexYJqhbJOWBYzQREREREREREZERbNFERERERERERERGMNCUAkKIm4UQR4QQx4UQ70s6PxQNIcRuIcT3hRCHhBAHhRDvsab/uRDirBDicevfG7qWeb9VLo4IIX6la/orhBBPWp99RAghktgmCkcIcco6jo8LIfZb0zYLIb4rhDhm/dzUNT/LQ04JIX6i6xzwuBBiQQjxXp4fBocQ4jNCiAtCiKe6phk7HwghhoUQd1nTHxJC7I11A0mbS5n4kBDisBDiCSHEV4UQG63pe4UQq13nik92LcMykQMu5cHYNYLlIVtcysNdXWXhlBDicWs6zw85JtzvMZOtQ0gp+S/BfwCKAE4AeA6AMoADAG5MOl/8F8mx3gHg5dbvGwAcBXAjgD8H8EcO899olYdhADdY5aRofbYPwM8CEADuAfD6pLeP/wKViVMAttqm/Q8A77N+fx+AD7I8DNY/67pwHsD1PD8Mzj8Avwjg5QCe6ppm7HwA4HcBfNL6/VYAdyW9zfwXqEy8DkDJ+v2DXWVib/d8tnRYJnLwz6U8GLtGsDxk659TebB9/lcA/sz6neeHHP+D+z1monUItmhK3isBHJdSPiOlrAL4IoBbEs4TRUBKOSOlfNT6fRHAIQA7PRa5BcAXpZRrUsqTAI4DeKUQYgeASSnlA7L1bf8cgDdFm3uK0S0APmv9/lmsH1uWh8HxGgAnpJSnPeZhecgZKeU/Abhsm2zyfNCd1lcAvIat3dLNqUxIKb8jpaxbfz4IYJdXGiwT+eFyjnDDc0TOeZUH67j9GoAveKXB8pAPHveYidYhGGhK3k4AZ7r+noZ38IFywGpu+DIAD1mTfs9qBv+ZrmaNbmVjp/W7fTpljwTwHSHEI0KId1rTtkspZ4DWhQPANdZ0lofBcSt6K4c8Pwwuk+eDzjJWoOIqgC2R5Zzi8NtoPXFuu0EI8ZgQ4gdCiFdb01gm8s/UNYLlIT9eDWBWSnmsaxrPDwPAdo+ZaB2CgabkOUUC+SrAHBNCTAD4vwDeK6VcAPAJAM8F8FIAM2g1dQXcywbLTH78vJTy5QBeD+DdQohf9JiX5WEACCHKAN4I4MvWJJ4fyEmQ48+ykSNCiNsA1AHcaU2aAbBHSvkyAH8I4PNCiEmwTOSdyWsEy0N+/AZ6H1jx/DAAHO4xXWd1mGb8/MBAU/KmAezu+nsXgHMJ5YUiJoQYQusEcKeU8h8AQEo5K6VsSCmbAP4Ore6UgHvZmEZvU3mWmYySUp6zfl4A8FW0jv2s1XS13aT5gjU7y8NgeD2AR6WUswDPD2T0fNBZRghRAjAF9W44lCJCiLcD+FUAb7W6N8DqAjFn/f4IWmNuvAAsE7lm+BrB8pAD1rH7NwDuak/j+SH/nO4xkXAdgoGm5D0M4PlCiBusJ9m3Arg74TxRBKx+rJ8GcEhK+b+6pu/omu1fA2i/PeJuALdao/zfAOD5APZZTR8XhRCvstJ8G4Cvx7IRZIwQYlwIsaH9O1oDvD6F1nF/uzXb27F+bFkeBkPPU0ieHwaeyfNBd1pvBnBfO0hB2SGEuBnAnwB4o5RypWv6NiFE0fr9OWiViWdYJvLN8DWC5SEffhnAYSllp7/HrOwAAAFKSURBVAsUzw/55naPiaTrEH6jhfNfLCPFvwGt0eFPALgt6fzwX2TH+RfQamL4BIDHrX9vAHAHgCet6XcD2NG1zG1WuTiCrjdHAbgJrcrECQAfBSCS3j7+0y4Pz0HrjQ8HABxsf/fR6u/8PQDHrJ+bWR4G4x+AMQBzAKa6pvH8MCD/0AowzgCoofXk8B0mzwcARtDqknkcrbfKPCfpbea/QGXiOFrjZLTrEe23AP1b61pyAMCjAP4Vy0S+/rmUB2PXCJaHbP1zKg/W9NsBvMs2L88POf4H93vMROsQ7QWJiIiIiIiIiIhCYdc5IiIiIiIiIiIygoEmIiIiIiIiIiIygoEmIiIiIiIiIiIygoEmIiIiIiIiIiIygoEmIiIiIiIiIiIygoEmIiIiIiIiIiIygoEmIiIiIiIiIiIygoEmIiIiIiIiIiIy4v8DH21BAB4FUzIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "periods = find_periods(dataset_path)\n",
    "period = aggregate_periods(periods)\n",
    "df.plot()\n",
    "indices = np.arange(0, df.shape[0], period)\n",
    "plt.vlines(indices, df.min(axis=1).min()-5, df.max(axis=1).max()+5, color=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'anomaly_length': {'max': 0, 'median': 0, 'min': 0},\n",
       "  'contamination': 0.0,\n",
       "  'dataset_id': ['Daphnet', 'S01R01E0'],\n",
       "  'dimensions': 9,\n",
       "  'is_train': False,\n",
       "  'length': 19200,\n",
       "  'means': {'ankle_horiz_fwd': -237.45979166666666,\n",
       "   'ankle_horiz_lateral': 335.3857291666667,\n",
       "   'ankle_vert': 988.5178125,\n",
       "   'leg_horiz_fwd': -44.54505208333333,\n",
       "   'leg_horiz_lateral': 212.06390625,\n",
       "   'leg_vert': 986.2916145833333,\n",
       "   'trunk_horiz_fwd': -25.295364583333335,\n",
       "   'trunk_horiz_lateral': 63.12739583333333,\n",
       "   'trunk_vert': 1014.6571875},\n",
       "  'num_anomalies': 0,\n",
       "  'periods': {'trunk_vert': 18,\n",
       "   'trunk_horiz_lateral': 36,\n",
       "   'leg_horiz_fwd': 36,\n",
       "   'ankle_horiz_lateral': 23,\n",
       "   'ankle_horiz_fwd': 59,\n",
       "   'leg_vert': 34,\n",
       "   'leg_horiz_lateral': 10,\n",
       "   'ankle_vert': 36,\n",
       "   'trunk_horiz_fwd': 14},\n",
       "  'stationarities': {'ankle_horiz_fwd': 'stationary',\n",
       "   'ankle_horiz_lateral': 'stationary',\n",
       "   'ankle_vert': 'stationary',\n",
       "   'leg_horiz_fwd': 'stationary',\n",
       "   'leg_horiz_lateral': 'stationary',\n",
       "   'leg_vert': 'stationary',\n",
       "   'trunk_horiz_fwd': 'stationary',\n",
       "   'trunk_horiz_lateral': 'stationary',\n",
       "   'trunk_vert': 'difference_stationary'},\n",
       "  'stddevs': {'ankle_horiz_fwd': 568.2084917495653,\n",
       "   'ankle_horiz_lateral': 268.74135482273186,\n",
       "   'ankle_vert': 359.7860472376193,\n",
       "   'leg_horiz_fwd': 374.90718312628087,\n",
       "   'leg_horiz_lateral': 163.10579007033442,\n",
       "   'leg_vert': 189.48829532674057,\n",
       "   'trunk_horiz_fwd': 150.77621929530906,\n",
       "   'trunk_horiz_lateral': 119.56667652139181,\n",
       "   'trunk_vert': 177.7147905462605},\n",
       "  'trends': {'ankle_horiz_fwd': [],\n",
       "   'ankle_horiz_lateral': [],\n",
       "   'ankle_vert': [],\n",
       "   'leg_horiz_fwd': [],\n",
       "   'leg_horiz_lateral': [],\n",
       "   'leg_vert': [],\n",
       "   'trunk_horiz_fwd': [],\n",
       "   'trunk_horiz_lateral': [],\n",
       "   'trunk_vert': []}}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_filename = get_metadata_filename(root_path / df_datasets.loc[1, \"test_path\"])\n",
    "with meta_filename.open(\"r\") as fh:\n",
    "    metas = json.load(fh)\n",
    "metas\n",
    "for meta in metas:\n",
    "    if meta[\"is_train\"] == False:\n",
    "        meta[\"periods\"] = dict(periods)\n",
    "metas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot perform reduce with flexible type",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-b829fcb58661>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mperiods\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"min={periods.min()},median={np.median(periods)},max={periods.max()},lcm={np.lcm.reduce(periods)},gcd={np.gcd.reduce(periods)},mode={np.bincount(periods).argmax()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0maggregate_periods\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mperiods\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_amin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     41\u001b[0m def _amin(a, axis=None, out=None, keepdims=False,\n\u001b[1;32m     42\u001b[0m           initial=_NoValue, where=True):\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_minimum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot perform reduce with flexible type"
     ]
    }
   ],
   "source": [
    "periods = np.array(periods)\n",
    "print(f\"min={periods.min()},median={np.median(periods)},max={periods.max()},lcm={np.lcm.reduce(periods)},gcd={np.gcd.reduce(periods)},mode={np.bincount(periods).argmax()}\")\n",
    "aggregate_periods(periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python-r",
   "language": "python",
   "name": "python-r"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
